Source code for mldft.utils.utils

import warnings
from functools import wraps
from importlib.util import find_spec
from socket import gethostname
from typing import Any, Callable, Dict, Optional, Tuple

import rich
import torch
from omegaconf import DictConfig

from mldft.utils import rich_utils
from mldft.utils.log_utils import pylogger
from mldft.utils.log_utils.config_in_tensorboard import dict_to_tree

log = pylogger.RankedLogger(__name__, rank_zero_only=True)




def extras(cfg: DictConfig) -> None:
    """Applies optional utilities before the task is started.

    Utilities:
        - Ignoring python warnings
        - Setting tags from command line
        - Rich config printing

    :param cfg: A DictConfig object containing the config tree.
    """
    # return if no `extras` config
    if not cfg.get("extras"):
        log.warning("Extras config not found! <cfg.extras=null>")
        return

    # disable python warnings
    if cfg.extras.get("ignore_warnings"):
        log.info("Disabling python warnings! <cfg.extras.ignore_warnings=True>")
        warnings.filterwarnings("ignore")

    # prompt user to input tags from command line if none are provided in the config
    if cfg.extras.get("enforce_tags"):
        log.info("Enforcing tags! <cfg.extras.enforce_tags=True>")
        rich_utils.enforce_tags(cfg, save_to_file=True)

    if cfg.extras.get("hostname"):
        log.info("Adding hostname to config! <cfg.extras.hostname=True>")
        cfg["extras"]["hostname"] = gethostname()

    # pretty print config tree using Rich library
    if cfg.extras.get("print_config"):
        log.info("Printing config tree with Rich! <cfg.extras.print_config=True>")
        # rich_utils.print_config_tree(cfg, resolve=True, save_to_file=True)
        rich.print(dict_to_tree(cfg, guide_style="dim"))





def task_wrapper(task_func: Callable) -> Callable:
    """Optional decorator that controls the failure behavior when executing the task function.

    This wrapper can be used to:
        - make sure loggers are closed even if the task function raises an exception (prevents multirun failure)
        - save the exception to a `.log` file
        - mark the run as failed with a dedicated file in the `logs/` folder (so we can find and rerun it later)
        - etc. (adjust depending on your needs)

    Example:

    .. code-block:: python

        @utils.task_wrapper
        def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
            ...
            return metric_dict, object_dict

    :param task_func: The task function to be wrapped.

    :return: The wrapped task function.
    """

    def wrap(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        # execute the task
        try:
            metric_dict, object_dict = task_func(cfg=cfg)

        # things to do if exception occurs
        except Exception as ex:
            # save exception to `.log` file
            log.exception("")

            # some hyperparameter combinations might be invalid or cause out-of-memory errors
            # so when using hparam search plugins like Optuna, you might want to disable
            # raising the below exception to avoid multirun failure
            raise ex

        # things to always do after either success or exception
        finally:
            # display output dir path in terminal
            log.info(f"Output dir: {cfg.paths.output_dir}")

            # always close wandb run (even if exception occurs so multirun won't fail)
            if find_spec("wandb"):  # check if wandb is installed
                import wandb

                if wandb.run:
                    log.info("Closing wandb!")
                    wandb.finish()

        return metric_dict, object_dict

    return wrap





def set_default_torch_dtype(dtype):
    """Decorator to set default data type for torch operations.

    Sets the default data type for the decorated function and unsets it afterwards to the previous default.

    Args:
        dtype: The default data type to set. Either torch.float32 or torch.float64.
    """

    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            # Set default data type at the beginning
            previous_dtype = torch.get_default_dtype()
            torch.set_default_dtype(dtype)
            try:
                result = func(*args, **kwargs)
                return result
            finally:
                # Set default data type back to the original at the end
                torch.set_default_dtype(previous_dtype)

        return wrapper

    return decorator





def get_metric_value(metric_dict: Dict[str, Any], metric_name: Optional[str]) -> Optional[float]:
    """Safely retrieves value of the metric logged in LightningModule.

    :param metric_dict: A dict containing metric values.
    :param metric_name: If provided, the name of the metric to retrieve.
    :return: If a metric name was provided, the value of the metric.
    """
    if not metric_name:
        log.info("Metric name is None! Skipping metric value retrieval...")
        return None

    if metric_name not in metric_dict:
        raise KeyError(
            f"Metric value not found! {metric_name=} is not in available metric names: \n "
            f"{list(metric_dict.keys())}\n"
            "Make sure metric name logged in LightningModule is correct!\n"
            "Make sure `optimized_metric` name in `hparams_search` config is correct!"
        )

    metric_value = metric_dict[metric_name].item()
    log.info(f"Retrieved metric value! <{metric_name}={metric_value}>")

    return metric_value