nemo/lightning/nemo_logger.py

# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import os
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Optional, Union

import lightning.fabric as fl
import lightning.pytorch as pl
from lightning.pytorch.callbacks.model_checkpoint import ModelCheckpoint as PTLModelCheckpoint
from lightning.pytorch.loggers import Logger, TensorBoardLogger, WandbLogger

from nemo.lightning.io.mixin import IOMixin
from nemo.lightning.pytorch.callbacks import ModelCheckpoint
from nemo.utils import logging
from nemo.utils.app_state import AppState
from nemo.utils.import_utils import safe_import

lcp, HAVE_RES = safe_import('nvidia_resiliency_ext.ptl_resiliency.local_checkpoint_callback')


@dataclass
class NeMoLogger(IOMixin):
    """Logger for NeMo runs.

    Args:
        name (str): Name of the experiment.
        log_dir (Optional[str]): Directory to save logs.
        explicit_log_dir (Optional[str]): Explicit log directory.
        version (Optional[str]): Version of the experiment.
        use_datetime_version (bool): Whether to use datetime as version.
        log_local_rank_0_only (bool): Log only on local rank 0.
        log_global_rank_0_only (bool): Log only on global rank 0.
        files_to_copy (Optional[List[str]]): List of files to copy to log directory.
        update_logger_directory (bool): Whether to update logger directory to write to `exp_dir`.
            If True, the `save_dir` passed to the logger will be reconfigured to write to `exp_dir / save_dir`.
            This ensures that all output from an experiment is written to a common directory.
            If False, the logger's save_dir will not be overwritten.
            This argument applies only to TensorBoardLogger and WandbLogger instances.
        ckpt (Optional[ModelCheckpoint]): Model checkpoint callback.
        tensorboard: (Optional[TensorBoardLogger]): A PyTorch Lightning TensorBoardLogger instance
            to add to the trainer.
        wandb (Optional[WandbLogger]): A PyTorch Lightning WandBLogger instance
            to add to the trainer.
        extra_loggers(Optional[List[Logger]]): Any additional loggers to add to the trainer.
    """

    name: str = "default"
    log_dir: Optional[str] = None
    explicit_log_dir: Optional[str] = None
    version: Optional[str] = None
    use_datetime_version: bool = True
    log_local_rank_0_only: bool = False
    log_global_rank_0_only: bool = False
    files_to_copy: Optional[List[str]] = None
    update_logger_directory: bool = True
    ckpt: Optional[ModelCheckpoint] = None
    tensorboard: Optional[TensorBoardLogger] = None
    wandb: Optional[WandbLogger] = None
    extra_loggers: List[Logger] = field(default_factory=list)

    def __post_init__(self):
        if self.log_local_rank_0_only is True and self.log_global_rank_0_only is True:
            raise ValueError(
                "Cannot set both log_local_rank_0_only and log_global_rank_0_only"
                " to True. Please set either one or neither."
            )

    def setup(self, trainer: Union[pl.Trainer, fl.Fabric], resume_if_exists: bool = False, task_config=None):
        """Setup the logger for the experiment.

        Args:
            trainer (Union[pl.Trainer, fl.Fabric]): Trainer or Fabric instance.
            resume_if_exists (bool): Whether to resume if log directory exists.

        Returns:
            AppState: The application state with updated log directory and other settings.
        """
        from nemo.constants import NEMO_ENV_VARNAME_VERSION
        from nemo.utils.get_rank import is_global_rank_zero

        self.local_rank = trainer.local_rank
        self.global_rank = trainer.global_rank
        logging.rank = self.global_rank

        # If explicit log_dir was passed, short circuit
        if self.explicit_log_dir and isinstance(trainer, pl.Trainer):
            if trainer.logger is not None and not self.update_logger_directory:
                logging.warning(
                    (
                        "nemo logger received explicit_log_dir: {} and the pytorch lightning trainer "
                        "that was passed to nemo_logger container a logger, but "
                        "update_logger_directory is False. This means that the trainer's logger "
                        "directory may not match with the explicit_log_dir."
                    ).format(
                        self.explicit_log_dir,
                    )
                )
            if self.log_dir or self.version:
                logging.error(
                    (
                        "nemo logger received explicit_log_dir: {} and at least one of dir: {}"
                        "or version: {}. Please note that dir, name, and version will be ignored."
                    ).format(
                        self.explicit_log_dir,
                        self.log_dir,
                        self.version,
                    )
                )
            if is_global_rank_zero() and Path(self.explicit_log_dir).exists():
                logging.warning("NeMoLogger is logging to {}, but it already exists.".format(self.explicit_log_dir))
            log_dir, _dir, self.name, version = Path(self.explicit_log_dir), str(self.explicit_log_dir), "", ""

        else:
            # Default dir to ./nemo_experiments if None was passed
            _dir = self.log_dir
            if self.log_dir is None:
                _dir = str(Path.cwd() / "nemo_experiments")

            if not self.name:
                self.name = "default"

            version = self.version or os.environ.get(NEMO_ENV_VARNAME_VERSION, None)
            if not version:
                if resume_if_exists:
                    logging.warning(
                        "No version folders would be created under the log folder as " "'resume_if_exists' is enabled."
                    )
                    version = None
                elif is_global_rank_zero():
                    if self.use_datetime_version:
                        version = time.strftime("%Y-%m-%d_%H-%M-%S")
            if version:
                if is_global_rank_zero():
                    os.environ[NEMO_ENV_VARNAME_VERSION] = version

            log_dir = Path(_dir) / Path(str(self.name)) / Path("" if version is None else str(version))

        # update app_state with log_dir, exp_dir, etc
        app_state = AppState()
        app_state.log_dir = log_dir
        app_state.exp_dir = _dir
        app_state.name = self.name
        app_state.version = version
        app_state.cmd_args = sys.argv

        # Cannot limit creation to global zero as all ranks write to own log file
        os.makedirs(log_dir, exist_ok=True)
        logging.info("Experiments will be logged at {}".format(log_dir))

        if task_config and is_global_rank_zero():
            self._handle_task_config(task_config, log_dir)

        if isinstance(trainer, pl.Trainer):
            self._setup_trainer_loggers(trainer, _dir, version)
            self._setup_trainer_model_checkpoint(trainer, log_dir=log_dir, ckpt=self.ckpt)

        self._setup_files_to_move(log_dir, app_state)
        self._setup_file_logging(log_dir)

        return app_state

    def _setup_trainer_loggers(self, trainer, dir, version):
        loggers = [self.tensorboard, self.wandb, *self.extra_loggers]
        loggers = [logger for logger in loggers if logger is not None]

        if loggers:
            if trainer.logger is not None and not self.tensorboard:
                loggers = [trainer.logger] + loggers
            trainer._logger_connector.configure_logger(loggers)

        if self.update_logger_directory:
            for logger in trainer.loggers:
                if isinstance(logger, TensorBoardLogger):
                    logger._version = version or ""
                    logger._root_dir = Path(dir) / os.path.relpath(logger.save_dir)
                    logging.warning(
                        '"update_logger_directory" is True. Overwriting tensorboard'
                        ' logger "save_dir" to {}'.format(logger._root_dir)
                    )
                elif isinstance(logger, WandbLogger):
                    logger._id = version or ""
                    logger._save_dir = Path(dir) / logger.save_dir
                    logger._wandb_init["dir"] = Path(dir) / logger.save_dir
                    logging.warning(
                        '"update_logger_directory" is True. Overwriting wandb logger "save_dir" to {}'.format(
                            logger._save_dir,
                        )
                    )

    def _setup_trainer_model_checkpoint(self, trainer, log_dir, ckpt=None):
        if ckpt:
            _overwrite_i = None
            for i, callback in enumerate(trainer.callbacks):
                if isinstance(callback, PTLModelCheckpoint) and not isinstance(callback, lcp.LocalCheckpointCallback):
                    logging.warning(
                        "The Trainer already contains a ModelCheckpoint callback. " "This will be overwritten."
                    )
                    _overwrite_i = i
                    break
            if _overwrite_i is not None:
                trainer.callbacks[_overwrite_i] = ckpt
            else:
                trainer.callbacks.append(ckpt)

            if ckpt.monitor and "val" in ckpt.monitor:
                if (
                    trainer.max_epochs is not None
                    and trainer.max_epochs != -1
                    and trainer.max_epochs < trainer.check_val_every_n_epoch
                ):
                    logging.error(
                        (
                            "The checkpoint callback was told to monitor a validation value but "
                            "trainer.max_epochs({}) was less than trainer.check_val_every_n_epoch({})."
                            "It is very likely this run will fail with ModelCheckpoint(monitor='{}') "
                            "not found in the returned metrics. Please ensure that validation is "
                            "run within trainer.max_epochs."
                        ).format(
                            trainer.max_epochs,
                            trainer.check_val_every_n_epoch,
                            ckpt.monitor,
                        )
                    )
                elif trainer.max_steps is not None and trainer.max_steps != -1:
                    logging.warning(
                        (
                            "The checkpoint callback was told to monitor a validation value and "
                            "trainer's max_steps was set to {}. Please ensure that max_steps will run "
                            "for at least {} epochs to ensure that checkpointing will not error out."
                        ).format(
                            trainer.max_steps,
                            trainer.check_val_every_n_epoch,
                        )
                    )

        from nemo.lightning import MegatronStrategy

        for callback in trainer.callbacks:
            if isinstance(callback, PTLModelCheckpoint) and not isinstance(callback, lcp.LocalCheckpointCallback):
                if callback.dirpath is None:
                    callback.dirpath = Path(log_dir / "checkpoints")
                if callback.filename is None:
                    if isinstance(trainer.strategy, MegatronStrategy):
                        callback.filename = f"{self.name}--{{{callback.monitor}:.4f}}-{{epoch}}-{{consumed_samples}}"
                    else:
                        # For automodel we log global_step
                        callback.filename = f"{self.name}--{{{callback.monitor}:.4f}}-{{epoch}}-{{step}}"
                ModelCheckpoint.CHECKPOINT_NAME_LAST = callback.filename + "-last"

    def _handle_task_config(self, task_config, log_dir):
        try:
            from fiddle._src.experimental import serialization

            task_config.save_config_img(log_dir / "task.png")
            task_json = serialization.dump_json(task_config)
            with open(log_dir / "task.json", "w") as f:
                f.write(task_json)
        except Exception as e:
            logging.warning("Saving task config failed: {}. Skipping saving".format(e))

    def _setup_file_logging(self, log_dir):
        """Set up file logging based on rank settings."""
        from nemo.constants import NEMO_ENV_VARNAME_TESTING
        from nemo.utils.env_var_parsing import get_envbool
        from nemo.utils.mcore_logger import add_handlers_to_mcore_logger

        # This is set if the env var NEMO_TESTING is set to True.
        nemo_testing = get_envbool(NEMO_ENV_VARNAME_TESTING, False)
        log_file = log_dir / f"nemo_log_globalrank-{self.global_rank}_localrank-{self.local_rank}.txt"

        if self.log_local_rank_0_only and not nemo_testing and self.local_rank == 0:
            logging.add_file_handler(log_file)
        elif self.log_global_rank_0_only and not nemo_testing and self.global_rank == 0:
            logging.add_file_handler(log_file)
        elif not (self.log_local_rank_0_only or self.log_global_rank_0_only):
            logging.add_file_handler(log_file)

        add_handlers_to_mcore_logger()

    def _setup_files_to_move(self, log_dir, app_state):
        files_to_move = []
        if Path(log_dir).exists():
            for child in Path(log_dir).iterdir():
                if child.is_file():
                    files_to_move.append(child)

        app_state.files_to_move = files_to_move
        app_state.files_to_copy = self.files_to_copy