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#
# 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 pandas as pd
import torch
from esm import FastaBatchedDataset, pretrained
from tqdm.auto import tqdm
ESM_CONFIG = {
"esm2-3b": {
"type": "esm2",
"model_path": "esm2_t36_3B_UR50D.pt",
"emb_dim": 2560,
"n_layers": 36,
},
"esm2-3b-ism": {
"type": "esm2",
"model_path": "esm2_t36_3B_UR50D_ism.pt",
"emb_dim": 2560,
"n_layers": 36,
}, # https://www.biorxiv.org/content/10.1101/2024.11.08.622579v2
}
def _load_esm2_model(model_path):
if os.path.exists(model_path):
model, alphabet = pretrained.load_model_and_alphabet_local(model_path)
else:
model, alphabet = pretrained.load_model_and_alphabet(
os.path.splitext(os.path.basename(model_path))[0]
)
return model, alphabet
def load_esm_model(model_name, local_esm_dir="release_data/checkpoint"):
local_model_path = os.path.join(local_esm_dir, ESM_CONFIG[model_name]["model_path"])
if os.path.exists(local_model_path):
print("Try to load ESM language model from ", local_model_path)
if "ism" in model_name and not os.path.exists(local_model_path):
raise RuntimeError(
f"esm2-3b-ism model: {local_model_path} does not exist \n"
+ "this model can not be download from fair-esm, \n"
+ "download it from https://af3-dev.tos-cn-beijing.volces.com/release_model/esm2_t36_3B_UR50D_ism.pt"
)
if model_name.startswith("esm2"):
model, alphabet = _load_esm2_model(local_model_path)
model.eval()
if torch.cuda.is_available():
model = model.cuda()
return model, alphabet
def _check_files_exist(save_dir, labels):
return all(
[os.path.exists(os.path.join(save_dir, label + ".pt")) for label in labels]
)
def compute_ESM_embeddings(
model_name,
model,
alphabet,
labels,
sequences,
save_dir,
toks_per_batch=4096,
truncation_seq_length=1022,
):
if model_name.startswith("esm2"):
embeddings = compute_esm2_embeddings(
model,
alphabet,
labels,
sequences,
save_dir,
toks_per_batch,
truncation_seq_length,
)
return embeddings
# Adapt from Corso, Gabriele, et al. "Diffdock: Diffusion steps, twists, and turns for molecular docking."
# URL: https://github.com/gcorso/DiffDock/blob/main/utils/inference_utils.py
def compute_esm2_embeddings(
model,
alphabet,
labels,
sequences,
save_dir,
toks_per_batch=4096,
truncation_seq_length=1022,
):
dataset = FastaBatchedDataset(labels, sequences)
batches = dataset.get_batch_indices(toks_per_batch, extra_toks_per_seq=1)
data_loader = torch.utils.data.DataLoader(
dataset,
collate_fn=alphabet.get_batch_converter(truncation_seq_length),
batch_sampler=batches,
)
repr_layer = model.num_layers
embeddings = {}
with torch.no_grad():
for batch_idx, (labels, strs, toks) in enumerate(tqdm(data_loader)):
print(
f"Processing {batch_idx + 1} of {len(batches)} batches ({toks.size(0)} sequences)"
)
if _check_files_exist(save_dir, labels):
continue
if torch.cuda.is_available():
toks = toks.to(device="cuda", non_blocking=True)
out = model(toks, repr_layers=[repr_layer], return_contacts=False)
representation = out["representations"][repr_layer].to(device="cpu")
for i, label in enumerate(labels):
truncate_len = min(truncation_seq_length, len(strs[i]))
embeddings[label] = representation[i, 1 : truncate_len + 1].clone()
save_path = os.path.join(save_dir, label + ".pt")
torch.save(embeddings[label], save_path)
return embeddings
def pdb_sequences_iterator(
input_path="./scripts/msa/data/pdb_seqs/pdb_seq.csv",
save_path="./scripts/msa/data/pdb_seqs/pdb_labels_seqs.csv",
start_id=0,
end_id=-1,
):
if os.path.exists(save_path):
df_seq = pd.read_csv(save_path)
else:
df = pd.read_csv(input_path)
# Protein only
df = df[df["mol_type"] == "protein"]
# Sequence name
df["pdb_entity_id"] = df["pdb_id"] + "_" + df["entity_id"].astype(str)
# Group by 'seq'
df_seq = (
df.groupby("seq")["pdb_entity_id"]
.apply(lambda x: ",".join(x))
.reset_index()
)
# Use the first pdb_entity_id as the label
df_seq["seq_label"] = df_seq["pdb_entity_id"].apply(lambda x: x.split(",")[0])
assert df_seq["seq_label"].nunique() == len(df_seq)
# Get a part id
df_seq["part_id"] = df_seq["pdb_entity_id"].apply(lambda x: x[1:3])
df_seq.to_csv(save_path)
if end_id == -1:
end_id = len(df_seq)
df_seq = df_seq[start_id:end_id]
part_counts = dict(df_seq["part_id"].value_counts())
for part_id, count in part_counts.items():
df_part = df_seq[df_seq["part_id"] == part_id]
print(f"Part {part_id}: {len(df_part)} sequences.")
yield part_id, df_part["seq_label"].tolist(), df_part["seq"].tolist()
def process_pdb_dataset(
model_name, root_save_dir, pdb_seq_path, pdb_seq_label_path, start_id=0, end_id=-1
):
model, alphabet = load_esm_model(model_name)
seq_iterator = pdb_sequences_iterator(
pdb_seq_path, pdb_seq_label_path, start_id, end_id
)
error_parts = []
for part_id, labels, sequences in seq_iterator:
save_dir = os.path.join(root_save_dir, f"{part_id}")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print(f"[{part_id}] Generating ESM language model embeddings")
lm_embeddings = compute_ESM_embeddings(
model_name,
model,
alphabet,
labels,
sequences,
save_dir,
truncation_seq_length=4094,
toks_per_batch=16384,
)
print(f"[{part_id}] Processed {len(lm_embeddings)} sequences in total. Done!")
print("Error parts: ", error_parts)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, choices=list(ESM_CONFIG.keys()))
parser.add_argument("--start_id", type=int, default=0)
parser.add_argument("--end_id", type=int, default=-1)
args = parser.parse_args()
save_dir = f"./esm_embeddings/{args.model_name}"
pdb_seq_path = "./scripts/msa/data/pdb_seqs/pdb_seq.csv"
pdb_seq_label_path = "./scripts/msa/data/pdb_seqs/pdb_labels_seqs.csv"
if not os.path.exists(save_dir):
print("Make dir: ", save_dir)
os.makedirs(save_dir)
process_pdb_dataset(
args.model_name,
save_dir,
pdb_seq_path,
pdb_seq_label_path,
args.start_id,
args.end_id,
)
if __name__ == "__main__":
main()
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