train_ppiDCE.py

#!/usr/bin/env python3
"""
ppiDCE: Dual Cross-Encoder for PPI Classification.

Dependencies
------------
    conda create -n esm python=3.10 && conda activate esm
    pip install torch        # pick the CUDA build that matches your driver
    pip install transformers pandas tqdm

(Both training and inference use only the transformers and pandas packages
beyond PyTorch.)
"""
import argparse
import os
import torch
import torch.nn as nn
import pandas as pd
import logging
from torch.utils.data import Dataset, DataLoader
from transformers import EsmConfig, EsmTokenizer, EsmModel, logging as hf_logging
from tqdm import tqdm


def parse_args():
    parser = argparse.ArgumentParser(
        description='Train or fine-tune ppiDCE: dual cross-encoder PPI classifier.'
    )
    # Data
    parser.add_argument('--train_file',       type=str, required=True,
                        help='Path to training CSV: seq1,seq2,label')
    parser.add_argument('--val_file',         type=str, required=True,
                        help='Path to validation CSV: seq1,seq2,label')
    # Model
    parser.add_argument('--model_config',     type=str, required=True,
                        help='HuggingFace ESM model name or local path')
    parser.add_argument('--num_labels',       type=int, default=2,
                        help='Number of output labels (binary=2)')
    parser.add_argument('--from_scratch',     action='store_true',
                        help='Initialize ESM backbone randomly instead of loading pretrained')
    parser.add_argument('--num_layers',       type=int, default=None,
                        help='Total number of transformer layers when initializing from scratch')
    parser.add_argument('--freeze_layers',    type=int, default=0,
                        help='Number of bottom encoder layers to freeze (ignored if from_scratch)')
    parser.add_argument('--add_layers',       type=int, default=0,
                        help='Number of extra transformer layers to append')
    parser.add_argument('--suppress_warnings', action='store_true',
                        help='Suppress tokenizer truncation warnings')
    parser.add_argument('--checkpoint',       type=str, default=None,
                        help='Optional checkpoint (.pth) to load weights from')
    # Training
    parser.add_argument('--epochs',           type=int,   default=3,
                        help='Total training epochs')
    parser.add_argument('--batch_size',       type=int,   default=8,
                        help='Batch size for train/validation')
    parser.add_argument('--learning_rate',    type=float, default=2e-5,
                        help='Learning rate')
    parser.add_argument('--max_length',       type=int,   default=1024,
                        help='Max total tokens (seq1+seq2+special)')
    # Runtime
    parser.add_argument('--output_dir',       type=str, default='./',
                        help='Directory to save checkpoints and final model')
    parser.add_argument('--device',           type=str, default='cuda', choices=['cpu','cuda'],
                        help='Device for training')
    return parser.parse_args()

class PPICrossDataset(Dataset):
    def __init__(self, csv_file, tokenizer, max_length):
        self.df = pd.read_csv(csv_file)
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        seq1, seq2, lbl = self.df.iloc[idx]
        enc = self.tokenizer(
            seq1, seq2,
            truncation=True,
            padding='max_length',
            max_length=self.max_length,
            return_tensors='pt'
        )
        return {
            'input_ids':      enc.input_ids.squeeze(0),
            'attention_mask': enc.attention_mask.squeeze(0),
            'labels':         torch.tensor(int(lbl), dtype=torch.long)
        }

class ppiDCE(nn.Module):
    def __init__(self, esm_model, num_labels=2):
        super().__init__()
        self.esm = esm_model
        hidden_size = esm_model.config.hidden_size
        self.dropout = nn.Dropout(0.1)
        self.classifier = nn.Linear(hidden_size, num_labels)

    def forward(self, input_ids, attention_mask):
        outputs = self.esm(input_ids=input_ids, attention_mask=attention_mask)
        cls_token = outputs.last_hidden_state[:, 0, :]
        x = self.dropout(cls_token)
        return self.classifier(x)


def main():
    args = parse_args()

    # Optionally suppress tokenizer warnings
    if args.suppress_warnings:
        hf_logging.set_verbosity_error()
        logging.getLogger('transformers.tokenization_utils_base').setLevel(logging.ERROR)

    # Device setup
    device = torch.device(args.device if torch.cuda.is_available() and args.device=='cuda' else 'cpu')
    print(f"Using device: {device}")

    # Tokenizer & config
    tokenizer = EsmTokenizer.from_pretrained(args.model_config)
    config = EsmConfig.from_pretrained(args.model_config)

    # Set layers for scratch
    if args.from_scratch:
        if args.num_layers:
            config.num_hidden_layers = args.num_layers
        print(f"Initializing from scratch with {config.num_hidden_layers} layers")

    # Append layers
    if args.add_layers:
        config.num_hidden_layers += args.add_layers
        print(f"Total layers after appending: {config.num_hidden_layers}")

        # Load or init backbone with proper positional embeddings
    # First, adjust config for desired positional embeddings
    if args.from_scratch:
        # Build fresh model with config (including any num_layers modifications)
        esm_model = EsmModel(config)
        print("Initialized new ESM model from scratch.")
    else:
        # Instantiate model architecture with extended positional embeddings
        esm_model = EsmModel(config)
        # Load pretrained weights where shapes match
        print(f"Loading pretrained weights from {args.model_config} into extended model architecture...")
        pretrained = EsmModel.from_pretrained(args.model_config)
        pretrained_state = pretrained.state_dict()
        model_state = esm_model.state_dict()
        # Copy matching parameters
        for key, weight in pretrained_state.items():
            if key in model_state and pretrained_state[key].shape == model_state[key].shape:
                model_state[key] = weight
        esm_model.load_state_dict(model_state)
        print("Pretrained weights loaded for matching parameters.")

    # If args.max_length exceeds original model limit, ensure positional embeddings exist
    max_pos = esm_model.config.max_position_embeddings
    if args.max_length > max_pos:
        print(f"Extending positional embeddings from {max_pos} to {args.max_length}")
        old_embed = esm_model.embeddings.position_embeddings.weight.data
        new_embed = nn.Embedding(args.max_length, old_embed.size(1))
        # Copy old embeddings and init new ones
        new_embed.weight.data[:max_pos] = old_embed
        new_embed.weight.data[max_pos:] = old_embed.new_empty(args.max_length - max_pos, old_embed.size(1)).normal_(0.0, 0.02)
        esm_model.embeddings.position_embeddings = new_embed
        esm_model.config.max_position_embeddings = args.max_length

    # Dataset & loaders
    train_ds = PPICrossDataset(args.train_file, tokenizer, args.max_length)
    val_ds   = PPICrossDataset(args.val_file,   tokenizer, args.max_length)
    train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True)
    val_loader   = DataLoader(val_ds, batch_size=args.batch_size, shuffle=False)

    # Model instantiation
    model = ppiDCE(esm_model, num_labels=args.num_labels)
    if args.checkpoint:
        model.load_state_dict(torch.load(args.checkpoint, map_location='cpu'), strict=False)
        print(f"Loaded checkpoint: {args.checkpoint}")

    # Freeze layers
    if not args.from_scratch and args.freeze_layers > 0:
        for p in model.esm.embeddings.parameters(): p.requires_grad=False
        for i in range(min(args.freeze_layers, len(model.esm.encoder.layer))):
            for p in model.esm.encoder.layer[i].parameters(): p.requires_grad=False
        print(f"Frozen bottom {args.freeze_layers} layers")

    model.to(device)
    if torch.cuda.device_count()>1 and device.type=='cuda': model = nn.DataParallel(model)

    optimizer = torch.optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=args.learning_rate)
    criterion = nn.CrossEntropyLoss()

    os.makedirs(args.output_dir, exist_ok=True)

    # Training & validation
    for epoch in range(1, args.epochs + 1):
        print(f"\nEpoch {epoch}/{args.epochs}")
        model.train()
        total_loss = 0
        for batch in tqdm(train_loader, desc="Train"):
            optimizer.zero_grad()
            logits = model(batch['input_ids'].to(device), batch['attention_mask'].to(device))
            loss = criterion(logits, batch['labels'].to(device))
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        print(f"Train loss: {total_loss/len(train_loader):.4f}")

        model.eval()
        val_loss, correct, total = 0, 0, 0
        with torch.no_grad():
            for batch in tqdm(val_loader, desc="Val"):
                logits = model(batch['input_ids'].to(device), batch['attention_mask'].to(device))
                loss = criterion(logits, batch['labels'].to(device))
                val_loss += loss.item()
                preds = torch.argmax(logits, dim=1)
                correct += (preds == batch['labels'].to(device)).sum().item()
                total += len(preds)
        print(f"Val loss: {val_loss/len(val_loader):.4f}, Acc: {correct/total:.4f}")

        ckpt_path = os.path.join(args.output_dir, f"ppiDCE_epoch{epoch}.pth")
        torch.save(model.module.state_dict() if hasattr(model,'module') else model.state_dict(), ckpt_path)
        print(f"Saved checkpoint: {ckpt_path}")

    # Final save
    final_model = os.path.join(args.output_dir, "ppiDCE_final.pth")
    torch.save(model.module.state_dict() if hasattr(model,'module') else model.state_dict(), final_model)
    print(f"Saved final model: {final_model}")

if __name__ == '__main__':
    main()