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6019d52 38bce11 6019d52 38bce11 6019d52 38bce11 6019d52 38bce11 6019d52 38bce11 6019d52 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 | #!/usr/bin/env python3
"""Cache pinned frozen-encoder embeddings for a MitoInteract sample."""
from __future__ import annotations
import argparse
import json
import time
from collections import defaultdict
from pathlib import Path
import numpy as np
import torch
from transformers import AutoModel, AutoTokenizer
DEFAULT_PROTEIN_MODEL = "facebook/esm2_t12_35M_UR50D"
DEFAULT_PROTEIN_REVISION = "6fbf070e65b0b7291e7bbcd451118c216cff79d8"
DEFAULT_LIGAND_MODEL = "DeepChem/ChemBERTa-77M-MLM"
DEFAULT_LIGAND_REVISION = "ed8a5374f2024ec8da53760af91a33fb8f6a15ff"
def read_rows(path: Path, limit: int | None) -> list[dict]:
rows = []
with path.open() as handle:
for line in handle:
if line.strip():
rows.append(json.loads(line))
if limit and len(rows) >= limit:
break
return rows
def masked_mean(
last_hidden: torch.Tensor, attention: torch.Tensor, special: torch.Tensor
) -> torch.Tensor:
mask = attention.bool() & ~special.bool()
weights = mask.unsqueeze(-1).to(last_hidden.dtype)
return (last_hidden * weights).sum(dim=1) / weights.sum(dim=1).clamp_min(1)
def encode_texts(
model, tokenizer, texts: list[str], batch_size: int, device: torch.device
) -> np.ndarray:
outputs = []
for start in range(0, len(texts), batch_size):
batch = texts[start : start + batch_size]
encoded = tokenizer(
batch,
padding=True,
truncation=True,
max_length=min(getattr(tokenizer, "model_max_length", 512), 512),
return_special_tokens_mask=True,
return_tensors="pt",
)
special = encoded.pop("special_tokens_mask")
encoded = {key: value.to(device) for key, value in encoded.items()}
with torch.inference_mode():
hidden = model(**encoded).last_hidden_state
pooled = masked_mean(hidden, encoded["attention_mask"], special.to(device))
outputs.append(pooled.float().cpu().numpy())
return np.concatenate(outputs, axis=0)
def encode_proteins(
model,
tokenizer,
entities: list[tuple[str, str]],
batch_size: int,
device: torch.device,
chunk_residues: int,
) -> dict[str, np.ndarray]:
chunks: list[str] = []
owners: list[str] = []
weights: list[int] = []
for entity_id, sequence in entities:
for start in range(0, len(sequence), chunk_residues):
chunk = sequence[start : start + chunk_residues]
chunks.append(chunk)
owners.append(entity_id)
weights.append(len(chunk))
chunk_embeddings = []
for start in range(0, len(chunks), batch_size):
batch = chunks[start : start + batch_size]
encoded = tokenizer(
batch,
padding=True,
truncation=True,
max_length=chunk_residues + 2,
return_special_tokens_mask=True,
return_tensors="pt",
)
special = encoded.pop("special_tokens_mask")
encoded = {key: value.to(device) for key, value in encoded.items()}
with torch.inference_mode():
hidden = model(**encoded).last_hidden_state
pooled = masked_mean(hidden, encoded["attention_mask"], special.to(device))
chunk_embeddings.extend(pooled.float().cpu().numpy())
accum: dict[str, list[tuple[np.ndarray, int]]] = defaultdict(list)
for owner, embedding, weight in zip(owners, chunk_embeddings, weights, strict=True):
accum[owner].append((embedding, weight))
result = {}
for owner, values in accum.items():
matrix = np.stack([value for value, _ in values])
entity_weights = np.asarray([weight for _, weight in values], dtype=np.float32)
result[owner] = np.average(matrix, axis=0, weights=entity_weights)
return result
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"--sample", type=Path, default=Path("artifacts/dev-10k/sample.jsonl")
)
parser.add_argument("--target-key", default="paffinity")
parser.add_argument("--target-name", default="pAffinity")
parser.add_argument("--limit", type=int)
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--protein-chunk-residues", type=int, default=1022)
parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
parser.add_argument("--protein-model", default=DEFAULT_PROTEIN_MODEL)
parser.add_argument("--protein-revision", default=DEFAULT_PROTEIN_REVISION)
parser.add_argument("--ligand-model", default=DEFAULT_LIGAND_MODEL)
parser.add_argument("--ligand-revision", default=DEFAULT_LIGAND_REVISION)
parser.add_argument("--output", type=Path, default=Path("artifacts/embeddings.npz"))
args = parser.parse_args()
device_name = (
"cuda" if args.device == "auto" and torch.cuda.is_available() else args.device
)
if device_name == "auto":
device_name = "cpu"
device = torch.device(device_name)
rows = read_rows(args.sample, args.limit)
proteins = sorted({row["protein_id"]: row["sequence"] for row in rows}.items())
ligands = sorted({row["ligand_id"]: row["smiles"] for row in rows}.items())
started = time.monotonic()
protein_tokenizer = AutoTokenizer.from_pretrained(
args.protein_model, revision=args.protein_revision
)
protein_model = (
AutoModel.from_pretrained(args.protein_model, revision=args.protein_revision)
.eval()
.to(device)
)
protein_embeddings = encode_proteins(
protein_model,
protein_tokenizer,
proteins,
args.batch_size,
device,
args.protein_chunk_residues,
)
del protein_model
ligand_tokenizer = AutoTokenizer.from_pretrained(
args.ligand_model, revision=args.ligand_revision
)
ligand_model = (
AutoModel.from_pretrained(args.ligand_model, revision=args.ligand_revision)
.eval()
.to(device)
)
ligand_matrix = encode_texts(
ligand_model,
ligand_tokenizer,
[smiles for _, smiles in ligands],
args.batch_size,
device,
)
ligand_embeddings = {
entity_id: embedding
for (entity_id, _), embedding in zip(ligands, ligand_matrix, strict=True)
}
pair_protein = np.stack(
[protein_embeddings[row["protein_id"]] for row in rows]
).astype(np.float32)
pair_ligand = np.stack(
[ligand_embeddings[row["ligand_id"]] for row in rows]
).astype(np.float32)
args.output.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(
args.output,
pair_ids=np.asarray([row["pair_id"] for row in rows]),
target=np.asarray([row[args.target_key] for row in rows], dtype=np.float32),
protein=pair_protein,
ligand=pair_ligand,
)
metadata = {
"rows": len(rows),
"unique_proteins": len(proteins),
"unique_ligands": len(ligands),
"protein_dim": int(pair_protein.shape[1]),
"ligand_dim": int(pair_ligand.shape[1]),
"target_key": args.target_key,
"target_name": args.target_name,
"protein_model": args.protein_model,
"protein_revision": args.protein_revision,
"ligand_model": args.ligand_model,
"ligand_revision": args.ligand_revision,
"protein_chunk_residues": args.protein_chunk_residues,
"device": str(device),
"seconds": time.monotonic() - started,
}
args.output.with_suffix(".json").write_text(json.dumps(metadata, indent=2) + "\n")
print(json.dumps(metadata, indent=2))
if __name__ == "__main__":
main()
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