Source code for nemo.utils.exp_manager

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import os
import re
import subprocess
import sys
import time
import warnings
from copy import deepcopy
from dataclasses import dataclass
from datetime import timedelta
from pathlib import Path
from shutil import copy, move
from typing import Any, Dict, List, Optional, Union

import pytorch_lightning
import torch
from hydra.core.hydra_config import HydraConfig
from hydra.utils import get_original_cwd
from omegaconf import DictConfig, OmegaConf, open_dict
from pytorch_lightning.callbacks import Callback, ModelCheckpoint
from pytorch_lightning.callbacks.timer import Interval, Timer
from pytorch_lightning.loggers import LoggerCollection as _LoggerCollection
from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger
from pytorch_lightning.loops import TrainingEpochLoop
from pytorch_lightning.strategies.ddp import DDPStrategy

from nemo.constants import NEMO_ENV_VARNAME_TESTING, NEMO_ENV_VARNAME_VERSION
from nemo.utils import logging, timers
from nemo.utils.app_state import AppState
from nemo.utils.env_var_parsing import get_envbool
from nemo.utils.exceptions import NeMoBaseException
from nemo.utils.get_rank import is_global_rank_zero
from nemo.utils.lightning_logger_patch import add_filehandlers_to_pl_logger
from nemo.utils.model_utils import inject_model_parallel_rank, uninject_model_parallel_rank


class NotFoundError(NeMoBaseException):
    """ Raised when a file or folder is not found"""


class LoggerMisconfigurationError(NeMoBaseException):
    """ Raised when a mismatch between trainer.logger and exp_manager occurs"""

    def __init__(self, message):
        message = (
            message
            + " You can disable lighning's trainer from creating a logger by passing logger=False to its constructor."
        )
        super().__init__(message)


class CheckpointMisconfigurationError(NeMoBaseException):
    """ Raised when a mismatch between trainer.callbacks and exp_manager occurs"""


@dataclass
class CallbackParams:
    filepath: Optional[str] = None  # Deprecated
    dirpath: Optional[str] = None  # If None, exp_manager will attempt to handle the filepath
    filename: Optional[str] = None  # If None, exp_manager will attempt to handle the filepath
    monitor: Optional[str] = "val_loss"
    verbose: Optional[bool] = True
    save_last: Optional[bool] = True
    save_top_k: Optional[int] = 3
    save_weights_only: Optional[bool] = False
    mode: Optional[str] = "min"
    every_n_epochs: Optional[int] = 1
    prefix: Optional[str] = None  # If None, exp_manager will attempt to handle the filepath
    postfix: str = ".nemo"
    save_best_model: bool = False
    always_save_nemo: bool = False
    save_nemo_on_train_end: Optional[bool] = True  # Whether to automatically save .nemo file durin on_train_end hook
    model_parallel_size: Optional[int] = None  # tensor parallel size * pipeline parallel size


@dataclass
class StepTimingParams:
    reduction: Optional[str] = "mean"
    # if True torch.cuda.synchronize() is called on start/stop
    sync_cuda: Optional[bool] = False
    # if positive, defines the size of a sliding window for computing mean
    buffer_size: Optional[int] = 1


@dataclass
class ExpManagerConfig:
    # Log dir creation parameters
    explicit_log_dir: Optional[str] = None
    exp_dir: Optional[str] = None
    name: Optional[str] = None
    version: Optional[str] = None
    use_datetime_version: Optional[bool] = True
    resume_if_exists: Optional[bool] = False
    resume_past_end: Optional[bool] = False
    resume_ignore_no_checkpoint: Optional[bool] = False
    # Logging parameters
    create_tensorboard_logger: Optional[bool] = True
    summary_writer_kwargs: Optional[Dict[Any, Any]] = None
    create_wandb_logger: Optional[bool] = False
    wandb_logger_kwargs: Optional[Dict[Any, Any]] = None
    # Checkpointing parameters
    create_checkpoint_callback: Optional[bool] = True
    checkpoint_callback_params: Optional[CallbackParams] = CallbackParams()
    # Additional exp_manager arguments
    files_to_copy: Optional[List[str]] = None
    # logs timing of train/val/test steps
    log_step_timing: Optional[bool] = True
    step_timing_kwargs: Optional[StepTimingParams] = StepTimingParams()
    # Configures creation of log files for different ranks
    log_local_rank_0_only: Optional[bool] = False
    log_global_rank_0_only: Optional[bool] = False
    # disable initial validation when resuming from a checkpoint saved during validation
    disable_validation_on_resume: Optional[bool] = True


class TimingCallback(Callback):
    """
    Logs execution time of train/val/test steps
    """

    def __init__(self, timer_kwargs={}):
        self.timer = timers.NamedTimer(**timer_kwargs)

    def _on_batch_start(self, name):
        # reset only if we do not return mean of a sliding window
        if self.timer.buffer_size <= 0:
            self.timer.reset(name)

        self.timer.start(name)

    def _on_batch_end(self, name, pl_module):
        self.timer.stop(name)
        pl_module.log(name, self.timer[name], on_step=True, on_epoch=False)

    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
        self._on_batch_start("train_step_timing")

    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
        self._on_batch_end("train_step_timing", pl_module)

    def on_validation_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
        self._on_batch_start("validation_step_timing")

    def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
        self._on_batch_end("validation_step_timing", pl_module)

    def on_test_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
        self._on_batch_start("test_step_timing")

    def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
        self._on_batch_end("test_step_timing", pl_module)

    def on_before_backward(self, trainer, pl_module, loss):
        self._on_batch_start("train_backward_timing")

    def on_after_backward(self, trainer, pl_module):
        self._on_batch_end("train_backward_timing", pl_module)


[docs]def exp_manager(trainer: 'pytorch_lightning.Trainer', cfg: Optional[Union[DictConfig, Dict]] = None) -> Path: """ exp_manager is a helper function used to manage folders for experiments. It follows the pytorch lightning paradigm of exp_dir/model_or_experiment_name/version. If the lightning trainer has a logger, exp_manager will get exp_dir, name, and version from the logger. Otherwise it will use the exp_dir and name arguments to create the logging directory. exp_manager also allows for explicit folder creation via explicit_log_dir. The version can be a datetime string or an integer. Datestime version can be disabled if use_datetime_version is set to False. It optionally creates TensorBoardLogger, WandBLogger, ModelCheckpoint objects from pytorch lightning. It copies sys.argv, and git information if available to the logging directory. It creates a log file for each process to log their output into. exp_manager additionally has a resume feature (resume_if_exists) which can be used to continuing training from the constructed log_dir. When you need to continue the training repeatedly (like on a cluster which you need multiple consecutive jobs), you need to avoid creating the version folders. Therefore from v1.0.0, when resume_if_exists is set to True, creating the version folders is ignored. Args: trainer (pytorch_lightning.Trainer): The lightning trainer. cfg (DictConfig, dict): Can have the following keys: - explicit_log_dir (str, Path): Can be used to override exp_dir/name/version folder creation. Defaults to None, which will use exp_dir, name, and version to construct the logging directory. - exp_dir (str, Path): The base directory to create the logging directory. Defaults to None, which logs to ./nemo_experiments. - name (str): The name of the experiment. Defaults to None which turns into "default" via name = name or "default". - version (str): The version of the experiment. Defaults to None which uses either a datetime string or lightning's TensorboardLogger system of using version_{int}. - use_datetime_version (bool): Whether to use a datetime string for version. Defaults to True. - resume_if_exists (bool): Whether this experiment is resuming from a previous run. If True, it sets trainer._checkpoint_connector.resume_from_checkpoint_fit_path so that the trainer should auto-resume. exp_manager will move files under log_dir to log_dir/run_{int}. Defaults to False. From v1.0.0, when resume_if_exists is True, we would not create version folders to make it easier to find the log folder for next runs. - resume_past_end (bool): exp_manager errors out if resume_if_exists is True and a checkpoint matching *end.ckpt indicating a previous training run fully completed. This behaviour can be disabled, in which case the *end.ckpt will be loaded by setting resume_past_end to True. Defaults to False. - resume_ignore_no_checkpoint (bool): exp_manager errors out if resume_if_exists is True and no checkpoint could be found. This behaviour can be disabled, in which case exp_manager will print a message and continue without restoring, by setting resume_ignore_no_checkpoint to True. Defaults to False. - create_tensorboard_logger (bool): Whether to create a tensorboard logger and attach it to the pytorch lightning trainer. Defaults to True. - summary_writer_kwargs (dict): A dictionary of kwargs that can be passed to lightning's TensorboardLogger class. Note that log_dir is passed by exp_manager and cannot exist in this dict. Defaults to None. - create_wandb_logger (bool): Whether to create a Weights and Baises logger and attach it to the pytorch lightning trainer. Defaults to False. - wandb_logger_kwargs (dict): A dictionary of kwargs that can be passed to lightning's WandBLogger class. Note that name and project are required parameters if create_wandb_logger is True. Defaults to None. - create_checkpoint_callback (bool): Whether to create a ModelCheckpoint callback and attach it to the pytorch lightning trainer. The ModelCheckpoint saves the top 3 models with the best "val_loss", the most recent checkpoint under *last.ckpt, and the final checkpoint after training completes under *end.ckpt. Defaults to True. - files_to_copy (list): A list of files to copy to the experiment logging directory. Defaults to None which copies no files. - log_local_rank_0_only (bool): Whether to only create log files for local rank 0. Defaults to False. Set this to True if you are using DDP with many GPUs and do not want many log files in your exp dir. - log_global_rank_0_only (bool): Whether to only create log files for global rank 0. Defaults to False. Set this to True if you are using DDP with many GPUs and do not want many log files in your exp dir. returns: log_dir (Path): The final logging directory where logging files are saved. Usually the concatenation of exp_dir, name, and version. """ # Add rank information to logger # Note: trainer.global_rank and trainer.is_global_zero are not set until trainer.fit, so have to hack around it local_rank = int(os.environ.get("LOCAL_RANK", 0)) global_rank = trainer.node_rank * trainer.num_devices + local_rank logging.rank = global_rank if cfg is None: logging.error("exp_manager did not receive a cfg argument. It will be disabled.") return if trainer.fast_dev_run: logging.info("Trainer was called with fast_dev_run. exp_manager will return without any functionality.") return # Ensure passed cfg is compliant with ExpManagerConfig schema = OmegaConf.structured(ExpManagerConfig) if isinstance(cfg, dict): cfg = OmegaConf.create(cfg) elif not isinstance(cfg, DictConfig): raise ValueError(f"cfg was type: {type(cfg)}. Expected either a dict or a DictConfig") cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True)) cfg = OmegaConf.merge(schema, cfg) error_checks(trainer, cfg) # Ensures that trainer options are compliant with NeMo and exp_manager arguments log_dir, exp_dir, name, version = get_log_dir( trainer=trainer, exp_dir=cfg.exp_dir, name=cfg.name, version=cfg.version, explicit_log_dir=cfg.explicit_log_dir, use_datetime_version=cfg.use_datetime_version, resume_if_exists=cfg.resume_if_exists, ) if cfg.resume_if_exists: check_resume(trainer, log_dir, cfg.resume_past_end, cfg.resume_ignore_no_checkpoint) checkpoint_name = name # If name returned from get_log_dir is "", use cfg.name for checkpointing if checkpoint_name is None or checkpoint_name == '': checkpoint_name = cfg.name or "default" cfg.name = name # Used for configure_loggers so that the log_dir is properly set even if name is "" cfg.version = version # update app_state with log_dir, exp_dir, etc app_state = AppState() app_state.log_dir = log_dir app_state.exp_dir = exp_dir app_state.name = name app_state.version = version app_state.checkpoint_name = checkpoint_name app_state.create_checkpoint_callback = cfg.create_checkpoint_callback app_state.checkpoint_callback_params = cfg.checkpoint_callback_params # Create the logging directory if it does not exist os.makedirs(log_dir, exist_ok=True) # Cannot limit creation to global zero as all ranks write to own log file logging.info(f'Experiments will be logged at {log_dir}') trainer._default_root_dir = log_dir if cfg.log_local_rank_0_only is True and cfg.log_global_rank_0_only is True: raise ValueError( f"Cannot set both log_local_rank_0_only and log_global_rank_0_only to True. Please set either one or neither." ) # This is set if the env var NEMO_TESTING is set to True. nemo_testing = get_envbool(NEMO_ENV_VARNAME_TESTING, False) # Handle logging to file # Logs local rank 0 only if local_rank == 0 and cfg.log_local_rank_0_only is True and nemo_testing is False: log_file = log_dir / f'nemo_log_globalrank-{global_rank}_localrank-{local_rank}.txt' logging.add_file_handler(log_file) # Logs only on global rank 0 elif global_rank == 0 and cfg.log_global_rank_0_only is True and not nemo_testing is False: log_file = log_dir / f'nemo_log_globalrank-{global_rank}_localrank-{local_rank}.txt' logging.add_file_handler(log_file) # Logs on all ranks. else: log_file = log_dir / f'nemo_log_globalrank-{global_rank}_localrank-{local_rank}.txt' logging.add_file_handler(log_file) # For some reason, LearningRateLogger requires trainer to have a logger. Safer to create logger on all ranks # not just global rank 0. if cfg.create_tensorboard_logger or cfg.create_wandb_logger: configure_loggers( trainer, exp_dir, cfg.name, cfg.version, cfg.create_tensorboard_logger, cfg.summary_writer_kwargs, cfg.create_wandb_logger, cfg.wandb_logger_kwargs, ) # add loggers timing callbacks if cfg.log_step_timing: timing_callback = TimingCallback(timer_kwargs=cfg.step_timing_kwargs or {}) trainer.callbacks.insert(0, timing_callback) if cfg.create_checkpoint_callback: configure_checkpointing( trainer, log_dir, checkpoint_name, cfg.resume_if_exists, cfg.checkpoint_callback_params ) if cfg.disable_validation_on_resume: # extend training loop to skip initial validation when resuming from checkpoint configure_no_restart_validation_training_loop(trainer) if is_global_rank_zero(): # Move files_to_copy to folder and add git information if present if cfg.files_to_copy: for _file in cfg.files_to_copy: copy(Path(_file), log_dir) # Create files for cmd args and git info with open(log_dir / 'cmd-args.log', 'w', encoding='utf-8') as _file: _file.write(" ".join(sys.argv)) # Try to get git hash git_repo, git_hash = get_git_hash() if git_repo: with open(log_dir / 'git-info.log', 'w', encoding='utf-8') as _file: _file.write(f'commit hash: {git_hash}') _file.write(get_git_diff()) # Add err_file logging to global_rank zero logging.add_err_file_handler(log_dir / 'nemo_error_log.txt') # Add lightning file logging to global_rank zero add_filehandlers_to_pl_logger(log_dir / 'lightning_logs.txt', log_dir / 'nemo_error_log.txt') return log_dir
def error_checks(trainer: 'pytorch_lightning.Trainer', cfg: Optional[Union[DictConfig, Dict]] = None): """ Checks that the passed trainer is compliant with NeMo and exp_manager's passed configuration. Checks that: - Throws error when hydra has changed the working directory. This causes issues with lightning's DDP - Throws error when trainer has loggers defined but create_tensorboard_logger or create_WandB_logger is True - Prints error messages when 1) run on multi-node and not Slurm, and 2) run on multi-gpu without DDP """ if HydraConfig.initialized() and get_original_cwd() != os.getcwd(): raise ValueError( "Hydra changed the working directory. This interferes with ExpManger's functionality. Please pass " "hydra.run.dir=. to your python script." ) if trainer.logger is not None and (cfg.create_tensorboard_logger or cfg.create_wandb_logger): raise LoggerMisconfigurationError( "The pytorch lightning trainer that was passed to exp_manager contained a logger, and either " f"create_tensorboard_logger: {cfg.create_tensorboard_logger} or create_wandb_logger: " f"{cfg.create_wandb_logger} was set to True. These can only be used if trainer does not already have a" " logger." ) if trainer.num_nodes > 1 and not check_slurm(trainer): logging.error( "You are running multi-node training without SLURM handling the processes." " Please note that this is not tested in NeMo and could result in errors." ) if trainer.num_devices > 1 and not isinstance(trainer.strategy, DDPStrategy): logging.error( "You are running multi-gpu without ddp.Please note that this is not tested in NeMo and could result in " "errors." ) def check_resume( trainer: 'pytorch_lightning.Trainer', log_dir: str, resume_past_end: bool = False, resume_ignore_no_checkpoint: bool = False, ): """Checks that resume=True was used correctly with the arguments pass to exp_manager. Sets trainer._checkpoint_connector.resume_from_checkpoint_fit_path as necessary. Returns: log_dir (Path): the log_dir exp_dir (str): the base exp_dir without name nor version name (str): The name of the experiment version (str): The version of the experiment Raises: NotFoundError: If resume is True, resume_ignore_no_checkpoint is False, and checkpoints could not be found. ValueError: If resume is True, and there were more than 1 checkpoint could found. """ if not log_dir: raise ValueError(f"Resuming requires the log_dir {log_dir} to be passed to exp_manager") checkpoint_dir = Path(Path(log_dir) / "checkpoints") checkpoint = None end_checkpoints = list(checkpoint_dir.rglob("*end.ckpt")) last_checkpoints = list(checkpoint_dir.rglob("*last.ckpt")) if not checkpoint_dir.exists(): if resume_ignore_no_checkpoint: logging.warning( f"There was no checkpoint folder at checkpoint_dir :{checkpoint_dir}. Training from scratch." ) return else: raise NotFoundError(f"There was no checkpoint folder at checkpoint_dir :{checkpoint_dir}. Cannot resume.") elif len(end_checkpoints) > 0: if resume_past_end: if len(end_checkpoints) > 1: if 'mp_rank' in str(end_checkpoints[0]): checkpoint = end_checkpoints[0] else: raise ValueError(f"Multiple checkpoints {end_checkpoints} that matches *end.ckpt.") logging.info(f"Resuming from {end_checkpoints[0]}") else: raise ValueError( f"Found {end_checkpoints[0]} indicating that the last training run has already completed." ) elif not len(last_checkpoints) > 0: if resume_ignore_no_checkpoint: logging.warning(f"There were no checkpoints found in {checkpoint_dir}. Training from scratch.") return else: raise NotFoundError(f"There were no checkpoints found in {checkpoint_dir}. Cannot resume.") elif len(last_checkpoints) > 1: if 'mp_rank' in str(last_checkpoints[0]) or 'tp_rank' in str(last_checkpoints[0]): checkpoint = last_checkpoints[0] checkpoint = uninject_model_parallel_rank(checkpoint) else: raise ValueError(f"Multiple checkpoints {last_checkpoints} that matches *last.ckpt.") else: logging.info(f"Resuming from {last_checkpoints[0]}") checkpoint = last_checkpoints[0] trainer._checkpoint_connector.resume_from_checkpoint_fit_path = str(checkpoint) if is_global_rank_zero(): # Check to see if any files exist that need to be moved files_to_move = [] for child in Path(log_dir).iterdir(): if child.is_file(): files_to_move.append(child) if len(files_to_move) > 0: # Move old files to a new folder other_run_dirs = Path(log_dir).glob("run_*") run_count = 0 for fold in other_run_dirs: if fold.is_dir(): run_count += 1 new_run_dir = Path(Path(log_dir) / f"run_{run_count}") new_run_dir.mkdir() for _file in files_to_move: move(str(_file), str(new_run_dir)) def check_explicit_log_dir( trainer: 'pytorch_lightning.Trainer', explicit_log_dir: [Path, str], exp_dir: str, name: str, version: str ) -> (Path, str, str, str): """ Checks that the passed arguments are compatible with explicit_log_dir. Returns: log_dir (Path): the log_dir exp_dir (str): the base exp_dir without name nor version name (str): The name of the experiment version (str): The version of the experiment Raise: LoggerMisconfigurationError """ if trainer.logger is not None: raise LoggerMisconfigurationError( "The pytorch lightning trainer that was passed to exp_manager contained a logger and explicit_log_dir: " f"{explicit_log_dir} was pass to exp_manager. Please remove the logger from the lightning trainer." ) # Checking only (explicit_log_dir) vs (exp_dir and version). # The `name` will be used as the actual name of checkpoint/archive. if exp_dir or version: logging.error( f"exp_manager received explicit_log_dir: {explicit_log_dir} and at least one of exp_dir: {exp_dir}, " f"or version: {version}. Please note that exp_dir, name, and version will be ignored." ) if is_global_rank_zero() and Path(explicit_log_dir).exists(): logging.warning(f"Exp_manager is logging to {explicit_log_dir}, but it already exists.") return Path(explicit_log_dir), str(explicit_log_dir), "", "" def get_log_dir( trainer: 'pytorch_lightning.Trainer', exp_dir: str = None, name: str = None, version: str = None, explicit_log_dir: str = None, use_datetime_version: bool = True, resume_if_exists: bool = False, ) -> (Path, str, str, str): """ Obtains the log_dir used for exp_manager. Returns: log_dir (Path): the log_dir exp_dir (str): the base exp_dir without name nor version name (str): The name of the experiment version (str): The version of the experiment explicit_log_dir (str): The explicit path to the log folder. Defaults to False. use_datetime_version (bool): Uses date and time as the version of the log folder. Defaults to True. resume_if_exists (bool): if resume_if_exists of the exp_manager's config is enabled or not. When enabled, the version folders would not get created. Raise: LoggerMisconfigurationError: If trainer is incompatible with arguments NotFoundError: If resume is True, resume_ignore_no_checkpoint is False, and checkpoints could not be found. ValueError: If resume is True, and there were more than 1 checkpoint could found. """ if explicit_log_dir: # If explicit log_dir was passed, short circuit return check_explicit_log_dir(trainer, explicit_log_dir, exp_dir, name, version) # Default exp_dir to ./nemo_experiments if None was passed _exp_dir = exp_dir if exp_dir is None: _exp_dir = str(Path.cwd() / 'nemo_experiments') # If the user has already defined a logger for the trainer, use the logger defaults for logging directory if trainer.logger is not None: if trainer.logger.save_dir: if exp_dir: raise LoggerMisconfigurationError( "The pytorch lightning trainer that was passed to exp_manager contained a logger, the logger's " f"save_dir was not None, and exp_dir ({exp_dir}) was not None. If trainer.logger.save_dir " "exists, exp_manager will use trainer.logger.save_dir as the logging directory and exp_dir " "must be None." ) _exp_dir = trainer.logger.save_dir if name: raise LoggerMisconfigurationError( "The pytorch lightning trainer that was passed to exp_manager contained a logger, and name: " f"{name} was also passed to exp_manager. If the trainer contains a " "logger, exp_manager will use trainer.logger.name, and name passed to exp_manager must be None." ) name = trainer.logger.name version = f"version_{trainer.logger.version}" # Use user-defined exp_dir, project_name, exp_name, and versioning options else: name = name or "default" version = 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 use_datetime_version: version = time.strftime('%Y-%m-%d_%H-%M-%S') else: tensorboard_logger = TensorBoardLogger(save_dir=Path(_exp_dir), name=name, version=version) version = f"version_{tensorboard_logger.version}" os.environ[NEMO_ENV_VARNAME_VERSION] = "" if version is None else version log_dir = Path(_exp_dir) / Path(str(name)) / Path("" if version is None else str(version)) return log_dir, str(_exp_dir), name, version def get_git_hash(): """ Helper function that tries to get the commit hash if running inside a git folder returns: Bool: Whether the git subprocess ran without error str: git subprocess output or error message """ try: return ( True, subprocess.check_output(['git', 'rev-parse', 'HEAD'], stderr=subprocess.STDOUT).decode(), ) except subprocess.CalledProcessError as err: return False, "{}\n".format(err.output.decode("utf-8")) def get_git_diff(): """ Helper function that tries to get the git diff if running inside a git folder returns: Bool: Whether the git subprocess ran without error str: git subprocess output or error message """ try: return subprocess.check_output(['git', 'diff'], stderr=subprocess.STDOUT).decode() except subprocess.CalledProcessError as err: return "{}\n".format(err.output.decode("utf-8")) class LoggerList(_LoggerCollection): """ A thin wrapper on Lightning's LoggerCollection such that name and version are better aligned with exp_manager """ def __init__(self, _logger_iterable, nemo_name=None, nemo_version=""): super().__init__(_logger_iterable) self._nemo_name = nemo_name self._nemo_version = nemo_version @property def name(self) -> str: return self._nemo_name @property def version(self) -> str: return self._nemo_version def configure_loggers( trainer: 'pytorch_lightning.Trainer', exp_dir: [Path, str], name: str, version: str, create_tensorboard_logger: bool, summary_writer_kwargs: dict, create_wandb_logger: bool, wandb_kwargs: dict, ): """ Creates TensorboardLogger and/or WandBLogger and attach them to trainer. Raises ValueError if summary_writer_kwargs or wandb_kwargs are misconfigured. """ # Potentially create tensorboard logger and/or WandBLogger logger_list = [] if create_tensorboard_logger: if summary_writer_kwargs is None: summary_writer_kwargs = {} elif "log_dir" in summary_writer_kwargs: raise ValueError( "You cannot pass `log_dir` as part of `summary_writer_kwargs`. `log_dir` is handled by lightning's " "TensorBoardLogger logger." ) tensorboard_logger = TensorBoardLogger(save_dir=exp_dir, name=name, version=version, **summary_writer_kwargs) logger_list.append(tensorboard_logger) logging.info("TensorboardLogger has been set up") if create_wandb_logger: if wandb_kwargs is None: wandb_kwargs = {} if "name" not in wandb_kwargs and "project" not in wandb_kwargs: raise ValueError("name and project are required for wandb_logger") # Update the wandb save_dir if wandb_kwargs.get('save_dir', None) is None: wandb_kwargs['save_dir'] = exp_dir os.makedirs(wandb_kwargs['save_dir'], exist_ok=True) wandb_logger = WandbLogger(version=version, **wandb_kwargs) logger_list.append(wandb_logger) logging.info("WandBLogger has been set up") logger_list = ( LoggerList(logger_list, nemo_name=name, nemo_version=version) if len(logger_list) > 1 else logger_list[0] ) trainer._logger_connector.configure_logger(logger_list) class NeMoModelCheckpoint(ModelCheckpoint): """ Light wrapper around Lightning's ModelCheckpoint to force a saved checkpoint on train_end """ def __init__( self, always_save_nemo=False, save_nemo_on_train_end=True, save_best_model=False, postfix=".nemo", n_resume=False, model_parallel_size=None, **kwargs, ): # Parse and store "extended" parameters: save_best model and postfix. self.always_save_nemo = always_save_nemo self.save_nemo_on_train_end = save_nemo_on_train_end self.save_best_model = save_best_model if self.save_best_model and not self.save_nemo_on_train_end: logging.warning( ( "Found save_best_model is True and save_nemo_on_train_end is False. " "Set save_nemo_on_train_end to True to automatically save the best model." ) ) self.postfix = postfix self.previous_best_path = "" self.model_parallel_size = model_parallel_size # `prefix` is deprecated if 'prefix' in kwargs: self.prefix = kwargs.pop('prefix') else: self.prefix = "" # Call the parent class constructor with the remaining kwargs. super().__init__(**kwargs) if self.save_top_k != -1 and n_resume: logging.debug("Checking previous runs") self.nemo_topk_check_previous_run() def nemo_topk_check_previous_run(self): try: self.best_k_models self.kth_best_model_path self.best_model_score self.best_model_path except AttributeError: raise AttributeError("Lightning's ModelCheckpoint was updated. NeMoModelCheckpoint will need an update.") self.best_k_models = {} self.kth_best_model_path = "" self.best_model_score = None self.best_model_path = "" checkpoints = list(Path(self.dirpath).rglob("*.ckpt")) for checkpoint in checkpoints: if 'mp_rank' in str(checkpoint) or 'tp_rank' in str(checkpoint): checkpoint = uninject_model_parallel_rank(checkpoint) checkpoint = str(checkpoint) if checkpoint[-10:] == '-last.ckpt': continue index = checkpoint.find(self.monitor) + len(self.monitor) + 1 # Find monitor in str + 1 for '=' if index != -1: match = re.search('[A-z]', checkpoint[index:]) if match: value = checkpoint[index : index + match.start() - 1] # -1 due to separator hypen self.best_k_models[checkpoint] = float(value) if len(self.best_k_models) < 1: return # No saved checkpoints yet _reverse = False if self.mode == "min" else True best_k_models = sorted(self.best_k_models, key=self.best_k_models.get, reverse=_reverse) ### This section should be ok as rank zero will delete all excess checkpoints, since all other ranks are ### instantiated after rank zero. models_to_delete should be 0 for all other ranks. if self.model_parallel_size is not None: models_to_delete = len(best_k_models) - self.model_parallel_size * self.save_top_k else: models_to_delete = len(best_k_models) - self.save_top_k logging.debug(f'Number of models to delete: {models_to_delete}') for _ in range(models_to_delete): model = best_k_models.pop(-1) self.best_k_models.pop(model) self._del_model_without_trainer(model) logging.debug(f"Removed checkpoint: {model}") self.kth_best_model_path = best_k_models[-1] self.best_model_path = best_k_models[0] self.best_model_score = self.best_k_models[self.best_model_path] def on_save_checkpoint(self, trainer, pl_module, checkpoint): # output = None output = super().on_save_checkpoint(trainer, pl_module, checkpoint) if not self.always_save_nemo: return output else: # Load the best model and then re-save it app_state = AppState() if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1: raise ValueError(f'always_save_nemo is not implemented for model parallel models.') # since we are creating tarfile artifacts we need to update .nemo path app_state.model_restore_path = os.path.abspath( os.path.expanduser(os.path.join(self.dirpath, self.prefix + self.postfix)) ) if self.save_best_model: if not os.path.exists(self.best_model_path): return output if self.best_model_path == self.previous_best_path: return output self.previous_model_path = self.best_model_path old_state_dict = deepcopy(pl_module.state_dict()) checkpoint = torch.load(self.best_model_path, map_location='cpu') if 'state_dict' in checkpoint: checkpoint = checkpoint['state_dict'] # get a new instanace of the model pl_module.load_state_dict(checkpoint, strict=True) pl_module.save_to(save_path=app_state.model_restore_path) pl_module.load_state_dict(old_state_dict, strict=True) else: pl_module.save_to(save_path=app_state.model_restore_path) return output def on_train_end(self, trainer, pl_module): if trainer.fast_dev_run: return None # check if we need to save a last checkpoint manually as validation isn't always run based on the interval if self.save_last and trainer.val_check_interval != 0: should_save_last_checkpoint = False if isinstance(trainer.val_check_interval, float) and trainer.val_check_interval % trainer.global_step != 0: should_save_last_checkpoint = True if isinstance(trainer.val_check_interval, int) and trainer.global_step % trainer.val_check_interval != 0: should_save_last_checkpoint = True if should_save_last_checkpoint: monitor_candidates = self._monitor_candidates(trainer) super()._save_last_checkpoint(trainer, monitor_candidates) # Call parent on_train_end() to save the -last checkpoint super().on_train_end(trainer, pl_module) # Load the best model and then re-save it if self.save_best_model: # wait for all processes trainer.strategy.barrier("SaveBestCheckpointConnector.resume_end") if self.best_model_path == "": logging.warning( f"{self} was told to save the best checkpoint at the end of training, but no saved checkpoints " "were found. Saving latest model instead." ) else: self.best_model_path = trainer.strategy.broadcast(self.best_model_path) trainer._checkpoint_connector.restore(self.best_model_path) if self.save_nemo_on_train_end: pl_module.save_to(save_path=os.path.join(self.dirpath, self.prefix + self.postfix)) def _del_model_without_trainer(self, filepath: str) -> None: app_state = AppState() if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1: # filepath needs to be updated to include mp_rank filepath = inject_model_parallel_rank(filepath) # each model parallel rank needs to remove its model if is_global_rank_zero() or (app_state.model_parallel_size is not None and app_state.data_parallel_rank == 0): try: self._fs.rm(filepath) logging.info(f"Removed checkpoint: {filepath}") except: logging.info(f"Tried to remove checkpoint: {filepath} but failed.") def configure_checkpointing( trainer: 'pytorch_lightning.Trainer', log_dir: Path, name: str, resume: bool, params: 'DictConfig' ): """ Adds ModelCheckpoint to trainer. Raises CheckpointMisconfigurationError if trainer already has a ModelCheckpoint callback """ for callback in trainer.callbacks: if isinstance(callback, ModelCheckpoint): raise CheckpointMisconfigurationError( "The pytorch lightning trainer that was passed to exp_manager contained a ModelCheckpoint " "and create_checkpoint_callback was set to True. Please either set create_checkpoint_callback " "to False, or remove ModelCheckpoint from the lightning trainer" ) # Create the callback and attach it to trainer if "filepath" in params: if params.filepath is not None: logging.warning("filepath is deprecated. Please switch to dirpath and filename instead") if params.dirpath is None: params.dirpath = Path(params.filepath).parent if params.filename is None: params.filename = Path(params.filepath).name with open_dict(params): del params["filepath"] if params.dirpath is None: params.dirpath = Path(log_dir / 'checkpoints') if params.filename is None: params.filename = f'{name}--{{{params.monitor}:.4f}}-{{epoch}}' if params.prefix is None: params.prefix = name NeMoModelCheckpoint.CHECKPOINT_NAME_LAST = params.filename + '-last' logging.debug(params.dirpath) logging.debug(params.filename) logging.debug(params.prefix) if "val" in params.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(" f"{trainer.max_epochs}) was less than trainer.check_val_every_n_epoch({trainer.check_val_every_n_epoch}" f"). It is very likely this run will fail with ModelCheckpoint(monitor='{params.monitor}') not found " "in the returned metrics. Please ensure that validation is run within trainer.max_epochs." ) elif trainer.max_steps is not None: logging.warning( "The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to " f"{trainer.max_steps}. Please ensure that max_steps will run for at least " f"{trainer.check_val_every_n_epoch} epochs to ensure that checkpointing will not error out." ) checkpoint_callback = NeMoModelCheckpoint(n_resume=resume, **params) checkpoint_callback.last_model_path = trainer._checkpoint_connector.resume_from_checkpoint_fit_path or "" if 'mp_rank' in checkpoint_callback.last_model_path or 'tp_rank' in checkpoint_callback.last_model_path: checkpoint_callback.last_model_path = uninject_model_parallel_rank(checkpoint_callback.last_model_path) trainer.callbacks.append(checkpoint_callback) def check_slurm(trainer): try: return trainer.accelerator_connector.is_slurm_managing_tasks except AttributeError: return False class StatelessTimer(Timer): """Extension of PTL timers to be per run.""" def __init__(self, duration: timedelta = None, interval: str = Interval.step, verbose: bool = True,) -> None: super().__init__(duration, interval, verbose) # Override PTL Timer's state dict to not store elapsed time information so that we can restore and continue training. def state_dict(self) -> Dict[str, Any]: return {} def load_state_dict(self, state_dict: Dict[str, Any]) -> None: return def configure_no_restart_validation_training_loop(trainer: pytorch_lightning.Trainer) -> None: if type(trainer.fit_loop.epoch_loop) != TrainingEpochLoop: warnings.warn("Detected custom epoch loop. Skipping no validation on restart support.", UserWarning) return loop = SkipResumeTrainingValidationLoop(trainer.min_steps, trainer.max_steps) loop.trainer = trainer trainer.fit_loop.epoch_loop = loop class SkipResumeTrainingValidationLoop(TrainingEpochLoop): """ Extend the PTL Epoch loop to skip validating when resuming. This happens when resuming a checkpoint that has already run validation, but loading restores the training state before validation has run. """ def _should_check_val_fx(self) -> bool: if self.restarting and self.global_step % self.trainer.val_check_batch == 0: return False return super()._should_check_val_fx()