pretrain.py

"""
File: pretrain.py
-------------------
Pretrain the CodonTransformer model.

The dataset is a JSON file. You can use prepare_training_data from CodonData to
prepare the dataset. The repository README has a guide on how to prepare the
dataset and use this script.
"""

import argparse
import os

import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader
from transformers import BigBirdConfig, BigBirdForMaskedLM, PreTrainedTokenizerFast

from CodonTransformer.CodonUtils import (
    MAX_LEN,
    NUM_ORGANISMS,
    TOKEN2MASK,
    IterableJSONData,
)


class MaskedTokenizerCollator:
    def __init__(self, tokenizer):
        self.tokenizer = tokenizer

    def __call__(self, examples):
        tokenized = self.tokenizer(
            [ex["codons"] for ex in examples],
            return_attention_mask=True,
            return_token_type_ids=True,
            truncation=True,
            padding=True,
            max_length=MAX_LEN,
            return_tensors="pt",
        )

        seq_len = tokenized["input_ids"].shape[-1]
        species_index = torch.tensor([[ex["organism"]] for ex in examples])
        tokenized["token_type_ids"] = species_index.repeat(1, seq_len)

        inputs = tokenized["input_ids"]
        targets = inputs.clone()

        prob_matrix = torch.full(inputs.shape, 0.15)
        prob_matrix[inputs < 5] = 0.0
        selected = torch.bernoulli(prob_matrix).bool()

        # 80% of the time, replace masked input tokens with respective mask tokens
        replaced = torch.bernoulli(torch.full(selected.shape, 0.8)).bool() & selected
        inputs[replaced] = torch.tensor(
            list((map(TOKEN2MASK.__getitem__, inputs[replaced].numpy())))
        )

        # 10% of the time, we replace masked input tokens with random vector.
        randomized = (
            torch.bernoulli(torch.full(selected.shape, 0.1)).bool()
            & selected
            & ~replaced
        )
        random_idx = torch.randint(26, 90, inputs.shape, dtype=torch.long)
        inputs[randomized] = random_idx[randomized]

        tokenized["input_ids"] = inputs
        tokenized["labels"] = torch.where(selected, targets, -100)

        return tokenized


class plTrainHarness(pl.LightningModule):
    def __init__(self, model, learning_rate, warmup_fraction):
        super().__init__()
        self.model = model
        self.learning_rate = learning_rate
        self.warmup_fraction = warmup_fraction

    def configure_optimizers(self):
        optimizer = torch.optim.AdamW(
            self.model.parameters(),
            lr=self.learning_rate,
        )
        lr_scheduler = {
            "scheduler": torch.optim.lr_scheduler.OneCycleLR(
                optimizer,
                max_lr=self.learning_rate,
                total_steps=self.trainer.estimated_stepping_batches,
                pct_start=self.warmup_fraction,
            ),
            "interval": "step",
            "frequency": 1,
        }
        return [optimizer], [lr_scheduler]

    def training_step(self, batch, batch_idx):
        self.model.bert.set_attention_type("block_sparse")
        outputs = self.model(**batch)
        self.log_dict(
            dictionary={
                "loss": outputs.loss,
                "lr": self.trainer.optimizers[0].param_groups[0]["lr"],
            },
            on_step=True,
            prog_bar=True,
        )
        return outputs.loss


class EpochCheckpoint(pl.Callback):
    def __init__(self, checkpoint_dir, save_interval):
        super().__init__()
        self.checkpoint_dir = checkpoint_dir
        self.save_interval = save_interval

    def on_train_epoch_end(self, trainer, pl_module):
        current_epoch = trainer.current_epoch
        if current_epoch % self.save_interval == 0 or current_epoch == 0:
            checkpoint_path = os.path.join(
                self.checkpoint_dir, f"epoch_{current_epoch}.ckpt"
            )
            trainer.save_checkpoint(checkpoint_path)
            print(f"\nCheckpoint saved at {checkpoint_path}\n")


def main(args):
    """Pretrain the CodonTransformer model."""
    pl.seed_everything(args.seed)
    torch.set_float32_matmul_precision("medium")

    # Load the tokenizer and model
    tokenizer = PreTrainedTokenizerFast(
        tokenizer_file=args.tokenizer_path,
        bos_token="[CLS]",
        eos_token="[SEP]",
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
    )
    config = BigBirdConfig(
        vocab_size=len(tokenizer),
        type_vocab_size=NUM_ORGANISMS,
        sep_token_id=2,
    )
    model = BigBirdForMaskedLM(config=config)
    harnessed_model = plTrainHarness(model, args.learning_rate, args.warmup_fraction)

    # Load the training data
    train_data = IterableJSONData(args.train_data_path, dist_env="slurm")
    data_loader = DataLoader(
        dataset=train_data,
        collate_fn=MaskedTokenizerCollator(tokenizer),
        batch_size=args.batch_size,
        num_workers=0 if args.debug else args.num_workers,
        persistent_workers=False if args.debug else True,
    )

    # Setup trainer and callbacks
    save_checkpoint = EpochCheckpoint(args.checkpoint_dir, args.save_interval)
    trainer = pl.Trainer(
        default_root_dir=args.checkpoint_dir,
        strategy="ddp_find_unused_parameters_true",
        accelerator="gpu",
        devices=1 if args.debug else args.num_gpus,
        precision="16-mixed",
        max_epochs=args.max_epochs,
        deterministic=False,
        enable_checkpointing=True,
        callbacks=[save_checkpoint],
        accumulate_grad_batches=args.accumulate_grad_batches,
    )

    # Pretrain the model
    trainer.fit(harnessed_model, data_loader)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Pretrain the CodonTransformer model.")
    parser.add_argument(
        "--tokenizer_path",
        type=str,
        required=True,
        help="Path to the tokenizer model file",
    )
    parser.add_argument(
        "--train_data_path",
        type=str,
        required=True,
        help="Path to the training data JSON file",
    )
    parser.add_argument(
        "--checkpoint_dir",
        type=str,
        required=True,
        help="Directory where checkpoints will be saved",
    )
    parser.add_argument(
        "--batch_size", type=int, default=6, help="Batch size for training"
    )
    parser.add_argument(
        "--max_epochs", type=int, default=5, help="Maximum number of epochs to train"
    )
    parser.add_argument(
        "--num_workers", type=int, default=5, help="Number of workers for data loading"
    )
    parser.add_argument(
        "--accumulate_grad_batches",
        type=int,
        default=1,
        help="Number of batches to accumulate gradients",
    )
    parser.add_argument(
        "--num_gpus", type=int, default=16, help="Number of GPUs to use for training"
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Learning rate for the optimizer",
    )
    parser.add_argument(
        "--warmup_fraction",
        type=float,
        default=0.1,
        help="Fraction of total steps to use for warmup",
    )
    parser.add_argument(
        "--save_interval", type=int, default=5, help="Save checkpoint every N epochs"
    )
    parser.add_argument(
        "--seed", type=int, default=123, help="Random seed for reproducibility"
    )
    parser.add_argument("--debug", action="store_true", help="Enable debug mode")
    args = parser.parse_args()
    main(args)