code/bertint/bertint_v8_inference.py

"""
Bertint V8 Inference — Standalone Prediction Pipeline

Architecture (Final — CrossAttn + MeanPool + Concat):
    GLYCAN:  WURCS -> BPE -> Bertose (frozen) -> [B, Lg, 768] -> proj -> [B, Lg, 512]
    PROTEIN: ESM-C per-residue -> [B, Lp, 960] -> proj -> [B, Lp, 512]
                                    |
              2x CrossAttentionBlock(d=512, 8H, FFN=1024)
                                    |
              glycan_enriched -> mean(valid tokens) -> [B, 512]
              protein_enriched -> mean(valid residues) -> [B, 512]
                                    |
                         concat -> [B, 1024] -> MLP -> score
"""

import argparse
import json
import logging
import sys
from pathlib import Path
from typing import Dict, List, Optional, Tuple

import h5py
import numpy as np
import pandas as pd
import torch

from bertint_v8 import BertintV8, load_bertose_encoder
from dataset_v8 import load_bpe_tokenizer

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)


class BertintPredictor:
    """Standalone predictor for glycan-protein binding affinity."""

    def __init__(self, checkpoint_path, bertose_checkpoint, vocab_path,
                 protein_emb_path, device="auto", max_protein_length=1024):
        if device == "auto":
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        else:
            self.device = torch.device(device)
        logger.info(f"Using device: {self.device}")
        self.max_protein_length = max_protein_length

        logger.info(f"Loading BPE tokenizer from {vocab_path}")
        self.tokenizer = load_bpe_tokenizer(vocab_path)

        logger.info(f"Loading Bertose from {bertose_checkpoint}")
        bertose_config, seq_embeddings, seq_layers = load_bertose_encoder(
            bertose_checkpoint, freeze_layers=12)

        logger.info("Building Bertint V8 (CrossAttn + MeanPool + Concat)")
        self.model = BertintV8(
            seq_embeddings=seq_embeddings,
            seq_layers=seq_layers,
            glycan_dim=bertose_config.seq_hidden_size,
            protein_dim=960, shared_dim=512, num_heads=8,
            ffn_dim=1024, num_cross_layers=2, dropout=0.1,
            swe_slices=512, swe_ref_points=64, separate_swe=False,
            pooling_mode="mean", interaction_mode="concat",
            use_cross_attention=True,
        )

        logger.info(f"Loading checkpoint from {checkpoint_path}")
        state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
        missing, unexpected = self.model.load_state_dict(state_dict, strict=False)
        if missing:
            logger.warning("Missing checkpoint tensors: %d", len(missing))
        if unexpected:
            logger.warning("Unexpected checkpoint tensors: %d", len(unexpected))
        self.model.to(self.device)
        self.model.eval()
        logger.info("Model loaded and ready for inference")

        logger.info(f"Loading protein embeddings from {protein_emb_path}")
        self.protein_embs = {}
        with h5py.File(protein_emb_path, "r") as f:
            for key in f.keys():
                emb = torch.from_numpy(f[key][:]).float()
                if emb.dim() == 1:
                    emb = emb.unsqueeze(0)
                if emb.shape[0] > max_protein_length:
                    emb = emb[:max_protein_length]
                self.protein_embs[key.replace("|", "/")] = emb
        logger.info(f"  Loaded {len(self.protein_embs)} protein embeddings")

    def _tokenize_glycan(self, wurcs):
        tok = self.tokenizer.tokenize(wurcs, max_length=256)
        return {k: torch.tensor(tok[k], dtype=torch.long)
                for k in ["token_ids", "attention_mask", "branch_depths", "linkage_types"]}

    @torch.no_grad()
    def predict_single(self, wurcs, protein_id):
        if protein_id not in self.protein_embs:
            raise KeyError(f"Protein '{protein_id}' not found in embeddings.")
        tokens = self._tokenize_glycan(wurcs)
        protein_emb = self.protein_embs[protein_id]
        batch = {
            "token_ids": tokens["token_ids"].unsqueeze(0).to(self.device),
            "attention_mask": tokens["attention_mask"].unsqueeze(0).float().to(self.device),
            "branch_depths": tokens["branch_depths"].unsqueeze(0).to(self.device),
            "linkage_types": tokens["linkage_types"].unsqueeze(0).to(self.device),
            "protein_emb": protein_emb.unsqueeze(0).to(self.device),
            "protein_mask": torch.ones(1, protein_emb.shape[0]).to(self.device),
        }
        score = self.model(
            token_ids=batch["token_ids"], attention_mask=batch["attention_mask"],
            branch_depths=batch["branch_depths"], linkage_types=batch["linkage_types"],
            protein_emb=batch["protein_emb"], protein_mask=batch["protein_mask"],
        )
        return score.item()

    @torch.no_grad()
    def predict_batch(self, wurcs_list, protein_ids, batch_size=32):
        all_scores = []
        n = len(wurcs_list)
        for start in range(0, n, batch_size):
            end = min(start + batch_size, n)
            tokenized = [self._tokenize_glycan(w) for w in wurcs_list[start:end]]
            token_ids = torch.stack([t["token_ids"] for t in tokenized]).to(self.device)
            attn_mask = torch.stack([t["attention_mask"] for t in tokenized]).float().to(self.device)
            branch_d = torch.stack([t["branch_depths"] for t in tokenized]).to(self.device)
            link_t = torch.stack([t["linkage_types"] for t in tokenized]).to(self.device)
            missing = [pid for pid in protein_ids[start:end] if pid not in self.protein_embs]
            if missing:
                raise KeyError(f"{len(missing)} protein ids missing from embeddings, first: {missing[0]!r}")
            protein_embs = [self.protein_embs[pid] for pid in protein_ids[start:end]]
            max_len = max(e.shape[0] for e in protein_embs)
            dim = protein_embs[0].shape[1]
            prot_pad = torch.zeros(len(protein_embs), max_len, dim)
            prot_mask = torch.zeros(len(protein_embs), max_len)
            for i, emb in enumerate(protein_embs):
                prot_pad[i, :emb.shape[0]] = emb
                prot_mask[i, :emb.shape[0]] = 1.0
            scores = self.model(
                token_ids=token_ids, attention_mask=attn_mask,
                branch_depths=branch_d, linkage_types=link_t,
                protein_emb=prot_pad.to(self.device), protein_mask=prot_mask.to(self.device),
            )
            all_scores.extend(scores.cpu().tolist())
            if (start // batch_size) % 10 == 0:
                logger.info(f"  Predicted {end}/{n} pairs ({100*end/n:.1f}%)")
        return all_scores


def main():
    parser = argparse.ArgumentParser(description="Bertint V8 Inference")
    parser.add_argument("--checkpoint", required=True)
    parser.add_argument("--bertose_checkpoint", required=True)
    parser.add_argument("--vocab_path", required=True)
    parser.add_argument("--protein_emb_path", required=True)
    parser.add_argument("--device", default="auto")
    parser.add_argument("--wurcs", type=str, default=None)
    parser.add_argument("--protein_id", type=str, default=None)
    parser.add_argument("--input_csv", type=str, default=None)
    parser.add_argument("--output_csv", type=str, default=None)
    parser.add_argument("--batch_size", type=int, default=32)
    args = parser.parse_args()

    predictor = BertintPredictor(
        checkpoint_path=args.checkpoint, bertose_checkpoint=args.bertose_checkpoint,
        vocab_path=args.vocab_path, protein_emb_path=args.protein_emb_path,
        device=args.device,
    )

    if args.wurcs and args.protein_id:
        score = predictor.predict_single(args.wurcs, args.protein_id)
        print(f"\nPrediction: {score:.4f} (0=no binding, 1=strong)")
    elif args.input_csv:
        df = pd.read_csv(args.input_csv)
        scores = predictor.predict_batch(
            df["glycan_wurcs"].tolist(), df["protein_id"].tolist(),
            batch_size=args.batch_size)
        df["predicted_score"] = scores
        out = args.output_csv or args.input_csv.replace(".csv", "_predictions.csv")
        df.to_csv(out, index=False)
        logger.info(f"Saved {len(df)} predictions to {out}")
    else:
        parser.error("Provide --wurcs + --protein_id or --input_csv")

if __name__ == "__main__":
    main()