src/guidance/oligo/dataloader.py

import torch
import pandas as pd
import lightning.pytorch as pl

from transformers import AutoTokenizer
from torch.utils.data import Dataset, DataLoader

class OligomerDataset(Dataset):
    def __init__(self, config, data_path):
        self.config = config
        self.data = pd.read_csv(data_path)
        self.tokenizer = AutoTokenizer.from_pretrained(self.config.lm.pretrained_esm)

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

    def __getitem__(self, idx):
        sequence = self.data.iloc[idx]["Sequence"]

        tokens = self.tokenizer(
            sequence,
            return_tensors='pt',
            padding='max_length',
            truncation=True,
            max_length=self.config.data.max_seq_len,
        )

        labels = torch.tensor(self.data.iloc[idx]['Binary Label'], dtype=torch.float)

        return {
            "input_ids": tokens['input_ids'],
            "attention_mask": tokens['attention_mask'],
            "labels": labels
        }


def collate_fn(batch):
    input_ids = torch.stack([item['input_ids'].squeeze(0) for item in batch])
    masks = torch.stack([item['attention_mask'].squeeze(0) for item in batch])
    labels = torch.stack([item['labels'] for item in batch])

    return {
        'input_ids': input_ids,
        'attention_mask': masks,
        'labels': labels
    }


class OligomerDataModule(pl.LightningDataModule):
    def __init__(self, config, train_dataset, val_dataset, test_dataset, collate_fn=collate_fn):
        super().__init__()
        self.train_dataset = train_dataset
        self.val_dataset = val_dataset
        self.test_dataset = test_dataset
        self.collate_fn = collate_fn
        self.batch_size = config.data.batch_size

    def train_dataloader(self):
        return DataLoader(self.train_dataset,
                          batch_size=self.batch_size,
                          collate_fn=self.collate_fn,
                          num_workers=8,
                          pin_memory=True,
                          shuffle=True)
    
    def val_dataloader(self):
        return DataLoader(self.val_dataset,
                          batch_size=self.batch_size,
                          collate_fn=self.collate_fn,
                          num_workers=8,
                          pin_memory=True)
    
    def test_dataloader(self):
        return DataLoader(self.test_dataset,
                          batch_size=self.batch_size,
                          collate_fn=self.collate_fn,
                          num_workers=8,
                          pin_memory=True)
    

def get_datasets(config):
    """Helper method to grab datasets to quickly init data module in main.py"""
    train_dataset = OligomerDataset(config, config.data.train)
    val_dataset = OligomerDataset(config, config.data.val)
    test_dataset = OligomerDataset(config, config.data.test)
    
    return  {
        "train": train_dataset,
        "val": val_dataset,
        "test": test_dataset
    }