dpacman/scripts/train.py

from typing import Any, Dict, List, Optional, Tuple

import hydra
import torch
import rootutils
import lightning as L
from lightning import Callback, LightningDataModule, LightningModule, Trainer
from lightning.pytorch.loggers import Logger
from omegaconf import DictConfig

root = rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)

from dpacman.utils import (
    RankedLogger,
    extras,
    get_metric_value,
    instantiate_callbacks,
    instantiate_loggers,
    log_hyperparameters,
    task_wrapper,
)

log = RankedLogger(__name__, rank_zero_only=True)


def h100_settings():
    # Use TensorFloat-32 for float32 matmuls → big speedup with tiny accuracy tradeoff
    torch.set_float32_matmul_precision("high")  # or "medium" for even more speed

    # (optional; older PyTorch toggle)
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True


@task_wrapper
def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """trains model given checkpoint on a datamodule train set.

    This method is wrapped in optional @task_wrapper decorator, that controls the behavior during
    failure. Useful for multiruns, saving info about the crash, etc.

    :param cfg: DictConfig configuration composed by Hydra.
    :return: Tuple[dict, dict] with metrics and dict with all instantiated objects.
    """
    # set seed for random number generators in pytorch, numpy and python.random
    if cfg.get("seed"):
        L.seed_everything(cfg.seed, workers=True)

    log.info(f"Instantiating datamodule <{cfg.data_module._target_}>")
    datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data_module)

    log.info(f"Instantiating model <{cfg.model._target_}>")
    model: LightningModule = hydra.utils.instantiate(cfg.model)

    log.info("Instantiating callbacks...")
    callbacks: List[Callback] = instantiate_callbacks(cfg.get("callbacks"))

    log.info("Instantiating loggers...")
    logger: List[Logger] = instantiate_loggers(cfg.get("logger"))

    log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
    trainer: Trainer = hydra.utils.instantiate(
        cfg.trainer, callbacks=callbacks, logger=logger
    )

    object_dict = {
        "cfg": cfg,
        "datamodule": datamodule,
        "model": model,
        "callbacks": callbacks,
        "logger": logger,
        "trainer": trainer,
    }

    if logger:
        log.info("Logging hyperparameters!")
        log_hyperparameters(object_dict)

    if cfg.get("train"):
        log.info("Starting training!")
        trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))

    train_metrics = trainer.callback_metrics

    log.info("Training completed! Ready for testing.")

    if cfg.get("test"):
        log.info("Starting testing!")
        ckpt_path = trainer.checkpoint_callback.best_model_path
        if ckpt_path == "":
            log.warning("Best ckpt not found! Using current weights for testing...")
            ckpt_path = None
        trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
        log.info(f"Best ckpt path: {ckpt_path}")

    test_metrics = trainer.callback_metrics

    # merge train and test metrics
    metric_dict = {**train_metrics, **test_metrics}

    return metric_dict, object_dict


@hydra.main(
    version_base="1.3", config_path=str(root / "configs"), config_name="train.yaml"
)
def main(cfg: DictConfig) -> None:
    """Main entry point for evaluation.

    :param cfg: DictConfig configuration composed by Hydra.
    """
    # apply extra utilities
    # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
    extras(cfg)

    h100_settings()  # try using settings for faster h100s training

    # train the model
    metric_dict, _ = train(cfg)

    # safely retrieve metric value for hydra-based hyperparameter optimization
    metric_value = get_metric_value(
        metric_dict=metric_dict, metric_name=cfg.get("optimized_metric")
    )

    # return optimized metric
    return metric_value


if __name__ == "__main__":
    main()