api_prediction.py

import torch
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
import numpy as np
from sklearn.metrics import *
from omegaconf import OmegaConf
import os
import random

from mcts import MCTS
import esm
from encoders import AptaBLE
from utils import get_scores, API_Dataset, get_nt_esm_dataset
from accelerate import Accelerator
import glob
import os
import requests

from transformers import AutoTokenizer, AutoModelForMaskedLM



# accelerator = Accelerator(kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=True)]) # NOTE: Buggy | Disables unused parameter issue
accelerator = Accelerator()

class AptaBLE_Pipeline():
    """In-house API prediction score pipeline."""
    def __init__(self, lr, dropout, weight_decay, epochs, model_type, model_version, model_save_path, accelerate_save_path, tensorboard_logdir, *args, **kwargs):
        self.device = accelerator.device
        self.lr = lr
        self.weight_decay = weight_decay
        self.epochs = epochs
        self.model_type = model_type
        self.model_version = model_version
        self.model_save_path = model_save_path
        self.accelerate_save_path = accelerate_save_path
        self.tensorboard_logdir = tensorboard_logdir
        esm_prot_encoder, self.esm_alphabet = esm.pretrained.esm.pretrained.esm2_t33_650M_UR50D() # ESM-2 Encoder

        # Freeze ESM-2
        for name, param in esm_prot_encoder.named_parameters():
            param.requires_grad = False
        for name, param in esm_prot_encoder.named_parameters():
            if "layers.30" in name or "layers.31" in name or "layers.32" in name:
                param.requires_grad = True

        self.batch_converter = self.esm_alphabet.get_batch_converter(truncation_seq_length=1678)

        # self.nt_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-2.5b-1000g")
        # nt_encoder = AutoModelForMaskedLM.from_pretrained("InstaDeepAI/nucleotide-transformer-2.5b-1000g")
        self.nt_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-v2-50m-multi-species", trust_remote_code=True)
        nt_encoder = AutoModelForMaskedLM.from_pretrained("InstaDeepAI/nucleotide-transformer-v2-50m-multi-species", trust_remote_code=True)

        self.model = AptaBLE(
            apta_encoder=nt_encoder,
            prot_encoder=esm_prot_encoder,
            dropout=dropout,
            ).to(self.device)

        self.criterion = torch.nn.BCELoss().to(self.device)


    def train(self):
        print('Training the model!')

        # Initialize writer instance
        writer = SummaryWriter(log_dir=f"log/{self.model_type}/{self.model_version}")

        # Initialize early stopping
        self.early_stopper = EarlyStopper(3, 3)
        self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
        self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, [4, 7, 10], 0.1)


        # Configure pytorch objects for distributed environment (i.e. sharded dataloader, multiple copies of model, etc.)
        self.model, self.optimizer, self.train_loader, self.test_loader, self.bench_loader, self.scheduler = accelerator.prepare(self.model, self.optimizer, self.train_loader, self.test_loader, self.bench_loader, self.scheduler)
        best_loss = 100

        for epoch in range(1, self.epochs+1):
            self.model.train()
            loss_train, _, _ = self.batch_step(self.train_loader, train_mode=True)
            self.model.eval()
            self.scheduler.step()
            with torch.no_grad():
                loss_test, pred_test, target_test = self.batch_step(self.test_loader, train_mode=False)
                test_scores = get_scores(target_test, pred_test)
                print("\tTrain Loss: {: .6f}\tTest Loss: {: .6f}\tTest ACC: {:.6f}\tTest AUC: {:.6f}\tTest MCC: {:.6f}\tTest PR_AUC: {:.6f}\tF1: {:.6f}\n".format(loss_train ,loss_test, test_scores['acc'], test_scores['roc_auc'], test_scores['mcc'], test_scores['pr_auc'], test_scores['f1']))
            # stop_early = self.early_stopper.early_stop(loss_test)
            # Early stop - model has not improved on eval set.
            # if stop_early:
            #     break
            # Only do checkpointing after near-convergence
            if epoch > 2:
                with torch.no_grad():
                    loss_bench, pred_bench, target_bench = self.batch_step(self.bench_loader, train_mode=False)
                    bench_scores = get_scores(target_bench, pred_bench)
                    print("\Bench Loss: {: .6f}\Bench ACC: {:.6f}\Bench AUC: {:.6f}\tBench MCC: {:.6f}\tBench PR_AUC: {:.6f}\tBench F1: {:.6f}\n".format(loss_bench, bench_scores['acc'], bench_scores['roc_auc'], bench_scores['mcc'], bench_scores['pr_auc'], bench_scores['f1']))
                    writer.add_scalar("Loss/bench", loss_bench, epoch)
                    for k, v in bench_scores.items():
                        if isinstance(v, float):
                            writer.add_scalar(f'{k}/bench', bench_scores[k], epoch)
                # Checkpoint based off of benchmark criteria
                # If model has improved and early stopping patience counter was just reset:
                if bench_scores['mcc'] > 0.5 and test_scores['mcc'] > 0.5 and loss_bench < 0.9 and accelerator.is_main_process:
                    best_loss = loss_test
                    # Remove all other files
                    # for f in glob.glob(f'{self.model_save_path}/model*.pt'):
                    #     os.remove(f)
                    accelerator.save_state(self.accelerate_save_path)
                    model = accelerator.unwrap_model(self.model)
                    torch.save(model.state_dict(), f'{self.model_save_path}/model_epoch={epoch}.pt')
                    print(f'Model saved at {self.model_save_path}')
                    print(f'Accelerate statistics saved at {self.accelerate_save_path}!') # Access via accelerator.load_state("./output")

            # logging
            writer.add_scalar("Loss/train", loss_train, epoch)
            writer.add_scalar("Loss/test", loss_test, epoch)
            for k, v in test_scores.items():
                if isinstance(v, float):
                    writer.add_scalar(f'{k}/test', test_scores[k], epoch)

        print("Training finished | access tensorboard via 'tensorboard --logdir=runs'.")
        writer.flush()
        writer.close()

    def batch_step(self, loader, train_mode = True):
        loss_total = 0
        pred = np.array([])
        target = np.array([])
        for batch_idx, (apta, esm_prot, y, apta_attn, prot_attn) in enumerate(loader):
            if train_mode:
                self.optimizer.zero_grad()

            y_pred = self.predict(apta, esm_prot, apta_attn, prot_attn)
            y_true = torch.tensor(y, dtype=torch.float32).to(self.device) # not needed since accelerator modifies dataloader to automatically map input objects to correct dev
            loss = self.criterion(torch.flatten(y_pred), y_true)

            if train_mode:
                accelerator.backward(loss) # Accelerate backward() method scales gradients and uses appropriate backward method as configured across devices
                self.optimizer.step()

            loss_total += loss.item()

            pred = np.append(pred, torch.flatten(y_pred).clone().detach().cpu().numpy())
            target = np.append(target, torch.flatten(y_true).clone().detach().cpu().numpy())
            mode = 'train' if train_mode else 'eval'
            print(mode + "[{}/{}({:.0f}%)]".format(batch_idx, len(loader), 100. * batch_idx / len(loader)), end = "\r", flush=True)
        loss_total /= len(loader)
        return loss_total, pred, target

    def predict(self, apta, esm_prot, apta_attn, prot_attn):
        y_pred, _, _, _ = self.model(apta, esm_prot, apta_attn, prot_attn)
        return y_pred

    def inference(self, apta, prot, labels):
        """Perform inference on a batch of aptamer/protein pairs."""
        self.model.eval()

        max_length = 275#nt_tokenizer.model_max_length

        inputs = [(i, j) for i, j in zip(labels, prot)]
        _, _, prot_tokens = self.batch_converter(inputs)
        apta_toks = self.nt_tokenizer.batch_encode_plus(apta, return_tensors='pt', padding='max_length', max_length=max_length)['input_ids']
        apta_attention_mask = apta_toks != self.nt_tokenizer.pad_token_id

        # # truncating
        prot_tokenized = prot_tokens[:, :1680]
        # # padding
        prot_ex = torch.ones((prot_tokenized.shape[0], 1680), dtype=torch.int64)*self.esm_alphabet.padding_idx
        prot_ex[:, :prot_tokenized.shape[1]] = prot_tokenized
        prot_attention_mask = prot_ex != self.esm_alphabet.padding_idx

        loader = DataLoader(API_Dataset(apta_toks, prot_ex, labels, apta_attention_mask, prot_attention_mask), batch_size=1, shuffle=False)

        self.model, loader = accelerator.prepare(self.model, loader)
        with torch.no_grad():
            _, pred, _ = self.batch_step(loader, train_mode=False)

        return pred

    def recommend(self, target, n_aptamers, depth, iteration, verbose=True):

        candidates = []
        _, _, prot_tokens = self.batch_converter([(1, target)])
        prot_tokenized = torch.tensor(prot_tokens, dtype=torch.int64)
        # adjusting for max protein sequence length during model training
        encoded_targetprotein = torch.ones((prot_tokenized.shape[0], 1678), dtype=torch.int64)*self.esm_alphabet.padding_idx
        encoded_targetprotein[:, :prot_tokenized.shape[1]] = prot_tokenized
        encoded_targetprotein = encoded_targetprotein.to(self.device)

        mcts = MCTS(encoded_targetprotein, depth=depth, iteration=iteration, states=8, target_protein=target, device=self.device, esm_alphabet=self.esm_alphabet)

        for _ in range(n_aptamers):
            mcts.make_candidate(self.model)
            candidates.append(mcts.get_candidate())

            self.model.eval()
            with torch.no_grad():
                sim_seq = np.array([mcts.get_candidate()])
                print('first candidate: ', sim_seq)
                # apta = torch.tensor(rna2vec(sim_seq), dtype=torch.int64).to(self.device)
                apta = self.nt_tokenizer.batch_encode_plus(sim_seq, return_tensors='pt', padding='max_length', max_length=275)['input_ids']
                apta_attn = apta != self.nt_tokenizer.pad_token_id
                prot_attn = encoded_targetprotein != self.esm_alphabet.padding_idx
                score, _, _, _ = self.model(apta.to(self.device), encoded_targetprotein.to(self.device), apta_attn.to(self.device), prot_attn.to(self.device))
                
            if verbose:
                candidate = mcts.get_candidate()
                print("candidate:\t", candidate, "\tscore:\t", score)
                print("*"*80)
            mcts.reset()

    def set_data_for_training(self, filepath, batch_size):
        # ds_train, ds_test, ds_bench = get_nt_esm_dataset(filepath, self.nt_tokenizer, self.batch_converter, self.esm_alphabet)
        ds_train, ds_test, ds_bench = get_nt_esm_dataset(filepath, self.nt_tokenizer, self.batch_converter, self.esm_alphabet)

        self.train_loader = DataLoader(API_Dataset(ds_train[0], ds_train[1], ds_train[2], ds_train[3], ds_train[4]), batch_size=batch_size, shuffle=True)
        self.test_loader = DataLoader(API_Dataset(ds_test[0], ds_test[1], ds_test[2], ds_test[3], ds_test[4]), batch_size=batch_size, shuffle=False)
        self.bench_loader = DataLoader(API_Dataset(ds_bench[0], ds_bench[1], ds_bench[2], ds_bench[3], ds_bench[4]), batch_size=batch_size, shuffle=False)

class EarlyStopper:
    def __init__(self, patience=1, min_delta=0):
        self.patience = patience
        self.min_delta = min_delta
        self.counter = 0
        self.min_validation_loss = float('inf')

    def early_stop(self, validation_loss):
        if validation_loss < self.min_validation_loss:
            self.min_validation_loss = validation_loss
            self.counter = 0
        elif validation_loss > (self.min_validation_loss + self.min_delta):
            self.counter += 1
            if self.counter >= self.patience:
                return True
        return False
    
def seed_torch(seed=5471):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True

def main():
    conf = OmegaConf.load('config.yaml')
    hyperparameters = conf.hyperparameters
    logging = conf.logging

    lr = hyperparameters['lr']
    wd = hyperparameters['weight_decay']
    dropout = hyperparameters['dropout']
    batch_size = hyperparameters['batch_size']
    epochs = hyperparameters['epochs']
    model_type = logging['model_type']
    model_version = logging['model_version']
    model_save_path = logging['model_save_path']
    accelerate_save_path = logging['accelerate_save_path']
    tensorboard_logdir = logging['tensorboard_logdir']
    seed = hyperparameters['seed']

    if not os.path.exists(model_save_path):
        os.makedirs(model_save_path)

    seed_torch(seed=seed)

    pipeline = AptaBLE_Pipeline(
        lr=lr,
        weight_decay=wd,
        epochs=epochs,
        model_type=model_type,
        model_version=model_version,
        model_save_path=model_save_path,
        accelerate_save_path=accelerate_save_path,
        tensorboard_logdir=tensorboard_logdir,
        d_model=128,
        d_ff=512,
        n_layers=6,
        n_heads=8,
        dropout=dropout,
        load_best_pt=True, # already loads the pretrained model using the datasets included in repo -- no need to run the bottom two cells
        device='cuda',
        seed=seed)

    datapath = "./data/ABW_real_dna_aptamers_HC_v6.pkl"
    # datapath = './data/ABW_real_dna_aptamers_HC_neg_scrambles_neg_homology.pkl'

    pipeline.set_data_for_training(datapath, batch_size=batch_size)
    pipeline.train()

    endpoint = 'https://slack.atombioworks.com/hooks/t3y99qu6pi81frhwrhef1849wh'
    msg = {"text": "Model has finished training."}
    _ = requests.post(endpoint, 
        json=msg,
        headers={"Content-Type": "application/json"},
    )

    return

if __name__ == "__main__":
    # launch training w/ the following: "accelerate launch api_prediction.py [args]"
    main()