pytorch-lightning/pytorch_lightning/trainer/trainer.py

import inspect
import os
import logging as python_logging
from argparse import ArgumentParser, Namespace
from typing import Union, Optional, List, Dict, Tuple, Iterable, Any

import torch
import torch.distributed as torch_distrib
import torch.multiprocessing as mp
from torch.utils.data import DataLoader

from pytorch_lightning import _logger as log
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, Callback, ProgressBarBase
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.loggers import LightningLoggerBase
from pytorch_lightning.profiler import SimpleProfiler, PassThroughProfiler, BaseProfiler
from pytorch_lightning.trainer.seed import seed_everything
from pytorch_lightning.trainer.auto_mix_precision import TrainerAMPMixin
from pytorch_lightning.trainer.callback_config import TrainerCallbackConfigMixin
from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
from pytorch_lightning.trainer.deprecated_api import TrainerDeprecatedAPITillVer0_8, TrainerDeprecatedAPITillVer0_9
from pytorch_lightning.trainer.distrib_data_parallel import TrainerDDPMixin
from pytorch_lightning.trainer.distrib_parts import (
    TrainerDPMixin, parse_gpu_ids, determine_root_gpu_device, pick_multiple_gpus)
from pytorch_lightning.trainer.evaluation_loop import TrainerEvaluationLoopMixin
from pytorch_lightning.trainer.logging import TrainerLoggingMixin
from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
from pytorch_lightning.trainer.supporters import TensorRunningAccum
from pytorch_lightning.trainer.training_io import TrainerIOMixin
from pytorch_lightning.trainer.training_loop import TrainerTrainLoopMixin
from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
from pytorch_lightning.trainer.lr_finder import TrainerLRFinderMixin
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities import rank_zero_warn, parsing


try:
    from apex import amp
except ImportError:
    APEX_AVAILABLE = False
else:
    APEX_AVAILABLE = True

try:
    import torch_xla
    import torch_xla.core.xla_model as xm
    import torch_xla.distributed.xla_multiprocessing as xmp
except ImportError:
    XLA_AVAILABLE = False
else:
    XLA_AVAILABLE = True

try:
    import horovod.torch as hvd
except ImportError:
    HOROVOD_AVAILABLE = False
else:
    HOROVOD_AVAILABLE = True


class Trainer(
    TrainerIOMixin,
    TrainerOptimizersMixin,
    TrainerAMPMixin,
    TrainerDPMixin,
    TrainerDDPMixin,
    TrainerLoggingMixin,
    TrainerModelHooksMixin,
    TrainerTrainingTricksMixin,
    TrainerDataLoadingMixin,
    TrainerEvaluationLoopMixin,
    TrainerTrainLoopMixin,
    TrainerCallbackConfigMixin,
    TrainerCallbackHookMixin,
    TrainerLRFinderMixin,
    TrainerDeprecatedAPITillVer0_8,
    TrainerDeprecatedAPITillVer0_9,
):
    DEPRECATED_IN_0_8 = (
        'gradient_clip', 'nb_gpu_nodes', 'max_nb_epochs', 'min_nb_epochs',
        'add_row_log_interval', 'nb_sanity_val_steps', 'tng_tqdm_dic',
    )
    DEPRECATED_IN_0_9 = ('use_amp', 'show_progress_bar', 'training_tqdm_dict')

    def __init__(
            self,
            logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,
            checkpoint_callback: Union[ModelCheckpoint, bool] = True,
            early_stop_callback: Optional[Union[EarlyStopping, bool]] = False,
            callbacks: Optional[List[Callback]] = None,
            default_root_dir: Optional[str] = None,
            gradient_clip_val: float = 0,
            process_position: int = 0,
            num_nodes: int = 1,
            num_processes: int = 1,
            gpus: Optional[Union[List[int], str, int]] = None,
            auto_select_gpus: bool = False,
            num_tpu_cores: Optional[int] = None,
            log_gpu_memory: Optional[str] = None,
            progress_bar_refresh_rate: int = 1,
            overfit_pct: float = 0.0,
            track_grad_norm: int = -1,
            check_val_every_n_epoch: int = 1,
            fast_dev_run: bool = False,
            accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1,
            max_epochs: int = 1000,
            min_epochs: int = 1,
            max_steps: Optional[int] = None,
            min_steps: Optional[int] = None,
            train_percent_check: float = 1.0,
            val_percent_check: float = 1.0,
            test_percent_check: float = 1.0,
            val_check_interval: float = 1.0,
            log_save_interval: int = 100,
            row_log_interval: int = 10,
            add_row_log_interval=None,  # backward compatible, todo: remove in v0.8.0
            distributed_backend: Optional[str] = None,
            precision: int = 32,
            print_nan_grads: bool = False,  # backward compatible, todo: remove in v0.9.0
            weights_summary: Optional[str] = 'full',
            weights_save_path: Optional[str] = None,
            num_sanity_val_steps: int = 2,
            truncated_bptt_steps: Optional[int] = None,
            resume_from_checkpoint: Optional[str] = None,
            profiler: Optional[Union[BaseProfiler, bool]] = None,
            benchmark: bool = False,
            deterministic: bool = False,
            reload_dataloaders_every_epoch: bool = False,
            auto_lr_find: Union[bool, str] = False,
            replace_sampler_ddp: bool = True,
            progress_bar_callback: Optional[Union[ProgressBarBase, bool]] = True,
            terminate_on_nan: bool = False,
            auto_scale_batch_size: Union[str, bool] = False,
            amp_level: str = 'O1',  # backward compatible, todo: remove in v0.8.0
            default_save_path=None,  # backward compatible, todo: remove in v0.8.0
            gradient_clip=None,  # backward compatible, todo: remove in v0.8.0
            nb_gpu_nodes=None,  # backward compatible, todo: remove in v0.8.0
            max_nb_epochs=None,  # backward compatible, todo: remove in v0.8.0
            min_nb_epochs=None,  # backward compatible, todo: remove in v0.8.0
            use_amp=None,  # backward compatible, todo: remove in v0.9.0
            show_progress_bar=None,  # backward compatible, todo: remove in v0.9.0
            nb_sanity_val_steps=None,  # backward compatible, todo: remove in v0.8.0
            **kwargs
    ):
        r"""

        Customize every aspect of training via flags

        Args:
            logger: Logger (or iterable collection of loggers) for experiment tracking.

            checkpoint_callback: Callback for checkpointing.

            early_stop_callback (:class:`pytorch_lightning.callbacks.EarlyStopping`):

            callbacks: Add a list of callbacks.

            default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed

            default_save_path:
                .. warning:: .. deprecated:: 0.7.3

                    Use `default_root_dir` instead. Will remove 0.9.0.

            gradient_clip_val: 0 means don't clip.

            gradient_clip:
                .. warning:: .. deprecated:: 0.7.0

                    Use `gradient_clip_val` instead. Will remove 0.9.0.

            process_position: orders the progress bar when running multiple models on same machine.

            num_nodes: number of GPU nodes for distributed training.

            nb_gpu_nodes:
                .. warning:: .. deprecated:: 0.7.0

                    Use `num_nodes` instead. Will remove 0.9.0.

            gpus: Which GPUs to train on.

            auto_select_gpus:

                If enabled and `gpus` is an integer, pick available
                gpus automatically. This is especially useful when
                GPUs are configured to be in "exclusive mode", such
                that only one process at a time can access them.

            num_tpu_cores: How many TPU cores to train on (1 or 8).

            log_gpu_memory: None, 'min_max', 'all'. Might slow performance

            show_progress_bar:
                .. warning:: .. deprecated:: 0.7.2

                        Set `progress_bar_refresh_rate` to positive integer to enable. Will remove 0.9.0.

            progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar.
                Ignored when a custom callback is passed to :paramref:`~Trainer.callbacks`.

            overfit_pct: How much of training-, validation-, and test dataset to check.

            track_grad_norm: -1 no tracking. Otherwise tracks that norm

            check_val_every_n_epoch: Check val every n train epochs.

            fast_dev_run: runs 1 batch of train, test  and val to find any bugs (ie: a sort of unit test).

            accumulate_grad_batches: Accumulates grads every k batches or as set up in the dict.

            max_epochs: Stop training once this number of epochs is reached.

            max_nb_epochs:
                .. warning:: .. deprecated:: 0.7.0

                    Use `max_epochs` instead. Will remove 0.9.0.

            min_epochs: Force training for at least these many epochs

            min_nb_epochs:
                .. warning:: .. deprecated:: 0.7.0

                    Use `min_epochs` instead. Will remove 0.9.0.

            max_steps: Stop training after this number of steps. Disabled by default (None).

            min_steps: Force training for at least these number of steps. Disabled by default (None).

            train_percent_check: How much of training dataset to check.

            val_percent_check: How much of validation dataset to check.

            test_percent_check: How much of test dataset to check.

            val_check_interval: How often within one training epoch to check the validation set

            log_save_interval: Writes logs to disk this often

            row_log_interval: How often to add logging rows (does not write to disk)

            add_row_log_interval:
                .. warning:: .. deprecated:: 0.7.0

                    Use `row_log_interval` instead. Will remove 0.9.0.

            distributed_backend: The distributed backend to use.

            use_amp:
                .. warning:: .. deprecated:: 0.7.0

                    Use `precision` instead. Will remove 0.9.0.

            precision: Full precision (32), half precision (16).

            print_nan_grads:
                .. warning:: .. deprecated:: 0.7.2

                    Has no effect. When detected, NaN grads will be printed automatically.
                    Will remove 0.9.0.

            weights_summary: Prints a summary of the weights when training begins.

            weights_save_path: Where to save weights if specified. Will override default_root_dir
                    for checkpoints only. Use this if for whatever reason you need the checkpoints
                    stored in a different place than the logs written in `default_root_dir`.

            amp_level: The optimization level to use (O1, O2, etc...).

            num_sanity_val_steps: Sanity check runs n batches of val before starting the training routine.

            nb_sanity_val_steps:
                .. warning:: .. deprecated:: 0.7.0

                    Use `num_sanity_val_steps` instead. Will remove 0.8.0.

            truncated_bptt_steps: Truncated back prop breaks performs backprop every k steps of

            resume_from_checkpoint: To resume training from a specific checkpoint pass in the path here.

            profiler:  To profile individual steps during training and assist in

            reload_dataloaders_every_epoch: Set to True to reload dataloaders every epoch

            auto_lr_find: If set to True, will `initially` run a learning rate finder,
                trying to optimize initial learning for faster convergence. Sets learning
                rate in self.hparams.lr | self.hparams.learning_rate in the lightning module.
                To use a different key, set a string instead of True with the key name.

            replace_sampler_ddp: Explicitly enables or disables sampler replacement.
                If not specified this will toggled automatically ddp is used

            benchmark: If true enables cudnn.benchmark.

            deterministic: If true enables cudnn.deterministic

            terminate_on_nan: If set to True, will terminate training (by raising a `ValueError`) at the
                end of each training batch, if any of the parameters or the loss are NaN or +/-inf.

            auto_scale_batch_size: If set to True, will `initially` run a batch size
                finder trying to find the largest batch size that fits into memory.
                The result will be stored in self.hparams.batch_size in the LightningModule.
                Additionally, can be set to either `power` that estimates the batch size through
                a power search or `binsearch` that estimates the batch size through a binary search.
        """

        self.deterministic = deterministic
        torch.backends.cudnn.deterministic = self.deterministic
        if self.deterministic:
            # fixing non-deterministic part of horovod
            # https://github.com/PyTorchLightning/pytorch-lightning/pull/1572/files#r420279383
            os.environ["HOROVOD_FUSION_THRESHOLD"] = str(0)

        # Init callbacks
        self.callbacks = callbacks or []
        self.on_init_start()

        # benchmarking
        self.benchmark = benchmark
        torch.backends.cudnn.benchmark = self.benchmark

        # Transfer params
        self.num_nodes = num_nodes
        # Backward compatibility, TODO: remove in v0.8.0
        if nb_gpu_nodes is not None:
            rank_zero_warn("Argument `nb_gpu_nodes` has renamed to `num_nodes` since v0.5.0"
                           " and this method will be removed in v0.8.0", DeprecationWarning)
            self.num_gpu_nodes = nb_gpu_nodes
        self.log_gpu_memory = log_gpu_memory

        self.gradient_clip_val = gradient_clip_val
        # Backward compatibility, TODO: remove in v0.8.0
        if gradient_clip is not None:
            rank_zero_warn("Argument `gradient_clip` has renamed to `gradient_clip_val` since v0.5.0"
                           " and this method will be removed in v0.8.0", DeprecationWarning)
            self.gradient_clip = gradient_clip

        self.check_val_every_n_epoch = check_val_every_n_epoch
        self.track_grad_norm = track_grad_norm
        self.on_gpu = True if (gpus and torch.cuda.is_available()) else False

        # tpu config
        self.on_tpu = num_tpu_cores is not None
        self.num_tpu_cores = num_tpu_cores
        assert num_tpu_cores in [1, 8, None], 'num_tpu_cores can only be 1 or 8'

        if num_processes != 1 and distributed_backend != "ddp_cpu":
            rank_zero_warn("num_processes is only used for distributed_backend=\"ddp_cpu\". Ignoring it.")
        self.num_processes = num_processes

        self.process_position = process_position
        self.weights_summary = weights_summary

        self.max_epochs = max_epochs
        # Backward compatibility, TODO: remove in v0.8.0
        if max_nb_epochs is not None:
            rank_zero_warn("Argument `max_nb_epochs` has renamed to `max_epochs` since v0.5.0"
                           " and this method will be removed in v0.8.0", DeprecationWarning)
            self.max_nb_epochs = max_nb_epochs

        self.min_epochs = min_epochs
        # Backward compatibility, TODO: remove in v0.8.0
        if min_nb_epochs is not None:
            rank_zero_warn("Argument `min_nb_epochs` has renamed to `min_epochs` since v0.5.0"
                           " and this method will be removed in v0.8.0", DeprecationWarning)
            self.min_nb_epochs = min_nb_epochs

        self.max_steps = max_steps
        self.min_steps = min_steps

        self.num_sanity_val_steps = num_sanity_val_steps
        # Backward compatibility, TODO: remove in v0.8.0
        if nb_sanity_val_steps is not None:
            rank_zero_warn("Argument `nb_sanity_val_steps` has renamed to "
                           "`num_sanity_val_steps` since v0.5.0"
                           " and this method will be removed in v0.8.0", DeprecationWarning)
            self.nb_sanity_val_steps = nb_sanity_val_steps

        # Backward compatibility, TODO: remove in v0.9.0
        if print_nan_grads:
            rank_zero_warn("Argument `print_nan_grads` has no effect and will be removed in v0.9.0."
                           " NaN grads will be printed automatically when detected.", DeprecationWarning)

        self.reload_dataloaders_every_epoch = reload_dataloaders_every_epoch

        self.auto_lr_find = auto_lr_find
        self.auto_scale_batch_size = auto_scale_batch_size
        self.replace_sampler_ddp = replace_sampler_ddp

        self.truncated_bptt_steps = truncated_bptt_steps
        self.resume_from_checkpoint = resume_from_checkpoint
        self.terminate_on_nan = terminate_on_nan
        self.shown_warnings = set()

        self.fast_dev_run = fast_dev_run
        if self.fast_dev_run:
            self.num_sanity_val_steps = 0
            self.max_epochs = 1
            log.info('Running in fast_dev_run mode: will run a full train,'
                     ' val and test loop using a single batch')

        # set default save path if user didn't provide one
        self.default_root_dir = default_root_dir

        # Backward compatibility, TODO: remove in v0.8.0
        if default_save_path is not None:
            self.default_root_dir = default_save_path

        if self.default_root_dir is None:
            self.default_root_dir = os.getcwd()

        # training bookeeping
        self.total_batch_idx = 0
        self.running_loss = TensorRunningAccum(window_length=20)
        self.batch_idx = 0
        self.progress_bar_metrics = {}
        self.callback_metrics = {}
        self.num_val_batches = 0
        self.num_training_batches = 0
        self.num_test_batches = 0
        self.train_dataloader = None
        self.test_dataloaders = None
        self.val_dataloaders = None

        # training state
        self.model = None
        self.testing = False
        self.disable_validation = False
        self.lr_schedulers = []
        self.optimizers = None
        self.optimizer_frequencies = []
        self.global_step = 0
        self.current_epoch = 0
        self.interrupted = False

        # configure logger
        self.configure_logger(logger)

        # configure profiler
        if profiler is True:
            profiler = SimpleProfiler()
        self.profiler = profiler or PassThroughProfiler()

        # configure early stop callback
        # creates a default one if none passed in
        self.configure_early_stopping(early_stop_callback)

        # configure checkpoint callback
        self.checkpoint_callback = checkpoint_callback
        self.weights_save_path = weights_save_path

        # accumulated grads
        self.accumulate_grad_batches = accumulate_grad_batches
        self.configure_accumulated_gradients(accumulate_grad_batches)

        # for gpus allow int, string and gpu list
        if auto_select_gpus and isinstance(gpus, int):
            self.gpus = pick_multiple_gpus(gpus)
        else:
            self.gpus = gpus

        self.data_parallel_device_ids = parse_gpu_ids(self.gpus)
        self.root_gpu = determine_root_gpu_device(self.data_parallel_device_ids)
        self.root_device = torch.device("cpu")

        # tpu state flags
        self.use_tpu = False
        self.tpu_local_core_rank = None
        self.tpu_global_core_rank = None

        # distributed backend choice
        self.distributed_backend = distributed_backend
        self.set_distributed_mode(distributed_backend)
        self._device = torch.device('cpu')

        # override dist backend when using tpus
        if self.on_tpu:
            self.init_tpu()
            self.current_tpu_idx = None

        # init flags for SLURM+ddp to work
        self.proc_rank = 0
        self.world_size = 1
        self.configure_slurm_ddp(self.num_nodes)
        self.node_rank = self.determine_ddp_node_rank()

        # nvidia setup
        self.set_nvidia_flags(self.is_slurm_managing_tasks, self.data_parallel_device_ids)

        # backward compatibility
        if show_progress_bar is not None:
            self.show_progress_bar = show_progress_bar

        self.progress_bar_refresh_rate = progress_bar_refresh_rate
        self.progress_bar_callback = progress_bar_callback
        self.configure_progress_bar()

        # logging
        self.log_save_interval = log_save_interval
        self.val_check_interval = val_check_interval

        # backward compatibility
        if add_row_log_interval is not None:
            rank_zero_warn("`add_row_log_interval` has renamed to `row_log_interval` since v0.5.0"
                           " and this method will be removed in v0.8.0", DeprecationWarning)
            if not row_log_interval:  # in case you did not set the proper value
                row_log_interval = add_row_log_interval
        self.row_log_interval = row_log_interval

        # how much of the data to use
        self.overfit_pct = overfit_pct
        self.determine_data_use_amount(train_percent_check, val_percent_check,
                                       test_percent_check, overfit_pct)

        # AMP init
        # These are the only lines needed after v0.8.0
        # we wrap the user's forward with autocast and give it back at the end of fit
        self.autocast_original_forward = None
        self.use_native_amp = hasattr(torch.cuda, "amp") and hasattr(torch.cuda.amp, "autocast")
        self.precision = precision
        self.scaler = None

        # TODO: remove for v0.8.0
        self.amp_level = amp_level
        self.init_amp(use_amp)

        self.on_colab_kaggle = os.getenv('COLAB_GPU') or os.getenv('KAGGLE_URL_BASE')

        # Callback system
        self.on_init_end()

    @property
    def slurm_job_id(self) -> int:
        try:
            job_id = os.environ['SLURM_JOB_ID']
            job_id = int(job_id)

            # in interactive mode, don't make logs use the same job id
            in_slurm_interactive_mode = os.environ['SLURM_JOB_NAME'] == 'bash'
            if in_slurm_interactive_mode:
                job_id = None

        except Exception:
            job_id = None
        return job_id

    @classmethod
    def default_attributes(cls):
        init_signature = inspect.signature(Trainer)

        args = {}
        for param_name in init_signature.parameters:
            value = init_signature.parameters[param_name].default
            args[param_name] = value

        return args

    @classmethod
    def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:
        r"""Scans the Trainer signature and returns argument names, types and default values.

        Returns:
            List with tuples of 3 values:
            (argument name, set with argument types, argument default value).

        Examples:
            >>> args = Trainer.get_init_arguments_and_types()
            >>> import pprint
            >>> pprint.pprint(sorted(args))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            [('accumulate_grad_batches',
              (<class 'int'>, typing.Dict[int, int], typing.List[list]),
              1),
             ...
             ('callbacks',
              (typing.List[pytorch_lightning.callbacks.base.Callback],
               <class 'NoneType'>),
               None),
             ('check_val_every_n_epoch', (<class 'int'>,), 1),
             ...
             ('max_epochs', (<class 'int'>,), 1000),
             ...
             ('precision', (<class 'int'>,), 32),
             ('print_nan_grads', (<class 'bool'>,), False),
             ('process_position', (<class 'int'>,), 0),
             ('profiler',
              (<class 'pytorch_lightning.profiler.profilers.BaseProfiler'>,
               <class 'bool'>,
               <class 'NoneType'>),
              None),
             ...
        """
        trainer_default_params = inspect.signature(cls).parameters
        name_type_default = []
        for arg in trainer_default_params:
            arg_type = trainer_default_params[arg].annotation
            arg_default = trainer_default_params[arg].default
            try:
                arg_types = tuple(arg_type.__args__)
            except AttributeError:
                arg_types = (arg_type,)

            name_type_default.append((arg, arg_types, arg_default))

        return name_type_default

    @classmethod
    def get_deprecated_arg_names(cls) -> List:
        """Returns a list with deprecated Trainer arguments."""
        depr_arg_names = []
        for name, val in cls.__dict__.items():
            if name.startswith('DEPRECATED') and isinstance(val, (tuple, list)):
                depr_arg_names.extend(val)
        return depr_arg_names

    @classmethod
    def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:
        r"""Extends existing argparse by default `Trainer` attributes.

        Args:
            parent_parser:
                The custom cli arguments parser, which will be extended by
                the Trainer default arguments.

        Only arguments of the allowed types (str, float, int, bool) will
        extend the `parent_parser`.

        Examples:
            >>> import argparse
            >>> import pprint
            >>> parser = argparse.ArgumentParser()
            >>> parser = Trainer.add_argparse_args(parser)
            >>> args = parser.parse_args([])
            >>> pprint.pprint(vars(args))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            {...
             'check_val_every_n_epoch': 1,
             'checkpoint_callback': True,
             'default_root_dir': None,
             'deterministic': False,
             'distributed_backend': None,
             'early_stop_callback': False,
             ...
             'logger': True,
             'max_epochs': 1000,
             'max_steps': None,
             'min_epochs': 1,
             'min_steps': None,
             ...
             'profiler': None,
             'progress_bar_callback': True,
             'progress_bar_refresh_rate': 1,
             ...}

        """
        parser = ArgumentParser(parents=[parent_parser], add_help=False, )

        blacklist = ['kwargs']
        depr_arg_names = cls.get_deprecated_arg_names() + blacklist

        allowed_types = (str, float, int, bool)

        # TODO: get "help" from docstring :)
        for arg, arg_types, arg_default in (at for at in cls.get_init_arguments_and_types()
                                            if at[0] not in depr_arg_names):
            arg_types = [at for at in allowed_types if at in arg_types]
            if not arg_types:
                # skip argument with not supported type
                continue
            arg_kwargs = {}
            if bool in arg_types:
                arg_kwargs.update(nargs="?")
                # if the only arg type is bool
                if len(arg_types) == 1:
                    # redefine the type for ArgParser needed
                    def use_type(x):
                        return bool(parsing.strtobool(x))
                else:
                    # filter out the bool as we need to use more general
                    use_type = [at for at in arg_types if at is not bool][0]
            else:
                use_type = arg_types[0]

            if arg == 'gpus':
                use_type = Trainer._allowed_type
                arg_default = Trainer._arg_default

            parser.add_argument(
                f'--{arg}',
                dest=arg,
                default=arg_default,
                type=use_type,
                help='autogenerated by pl.Trainer',
                **arg_kwargs,
            )

        return parser

    def _allowed_type(x) -> Union[int, str]:
        if ',' in x:
            return str(x)
        else:
            return int(x)

    def _arg_default(x) -> Union[int, str]:
        if ',' in x:
            return str(x)
        else:
            return int(x)

    @staticmethod
    def parse_argparser(arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:
        """Parse CLI arguments, required for custom bool types."""
        args = arg_parser.parse_args() if isinstance(arg_parser, ArgumentParser) else arg_parser
        args = {k: True if v is None else v for k, v in vars(args).items()}
        return Namespace(**args)

    @classmethod
    def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) -> 'Trainer':
        """create an instance from CLI arguments

        Example:
            >>> parser = ArgumentParser(add_help=False)
            >>> parser = Trainer.add_argparse_args(parser)
            >>> args = Trainer.parse_argparser(parser.parse_args(""))
            >>> trainer = Trainer.from_argparse_args(args)
        """
        if isinstance(args, ArgumentParser):
            args = Trainer.parse_argparser(args)
        params = vars(args)
        params.update(**kwargs)

        return cls(**params)

    @property
    def num_gpus(self) -> int:
        gpus = self.data_parallel_device_ids
        if gpus is None:
            return 0
        return len(gpus)

    @property
    def data_parallel(self) -> bool:
        return self.use_dp or self.use_ddp or self.use_ddp2

    @property
    def progress_bar_dict(self) -> dict:
        """ Read-only for progress bar metrics. """
        ref_model = self.model if not self.data_parallel else self.model.module
        return dict(**ref_model.get_progress_bar_dict(), **self.progress_bar_metrics)

    # -----------------------------
    # MODEL TRAINING
    # -----------------------------
    def fit(
            self,
            model: LightningModule,
            train_dataloader: Optional[DataLoader] = None,
            val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None
    ):
        r"""
        Runs the full optimization routine.

        Args:
            model: Model to fit.

            train_dataloader: A Pytorch
                DataLoader with training samples. If the model has
                a predefined train_dataloader method this will be skipped.

            val_dataloaders: Either a single
                Pytorch Dataloader or a list of them, specifying validation samples.
                If the model has a predefined val_dataloaders method this will be skipped

        Example::

            # Option 1,
            # Define the train_dataloader() and val_dataloader() fxs
            # in the lightningModule
            # RECOMMENDED FOR MOST RESEARCH AND APPLICATIONS TO MAINTAIN READABILITY
            trainer = Trainer()
            model = LightningModule()
            trainer.fit(model)

            # Option 2
            # in production cases we might want to pass different datasets to the same model
            # Recommended for PRODUCTION SYSTEMS
            train, val = DataLoader(...), DataLoader(...)
            trainer = Trainer()
            model = LightningModule()
            trainer.fit(model, train_dataloader=train, val_dataloader=val)

            # Option 1 & 2 can be mixed, for example the training set can be
            # defined as part of the model, and validation can then be feed to .fit()

        """
        # bind logger and other properties
        model.logger = self.logger
        self.copy_trainer_model_properties(model)

        # clean hparams
        if hasattr(model, 'hparams'):
            parsing.clean_namespace(model.hparams)

        # set up the passed in dataloaders (if needed)
        self.__attach_dataloaders(model, train_dataloader, val_dataloaders)

        # check that model is configured correctly
        self.check_model_configuration(model)

        # download the data and do whatever transforms we need
        # do before any spawn calls so that the model can assign properties
        # only on proc 0 because no spawn has happened yet
        model.prepare_data()

        # Run auto batch size scaling
        if self.auto_scale_batch_size:
            if isinstance(self.auto_scale_batch_size, bool):
                self.auto_scale_batch_size = 'power'
            self.scale_batch_size(model, mode=self.auto_scale_batch_size)

        # Run learning rate finder:
        if self.auto_lr_find:
            self._run_lr_finder_internally(model)

        # route to appropriate start method
        # when using multi-node or DDP within a node start each module in a separate process
        if self.use_ddp2:
            task = int(os.environ['SLURM_LOCALID'])
            self.ddp_train(task, model)
        elif self.use_ddp:
            if self.is_slurm_managing_tasks:
                task = int(os.environ['SLURM_LOCALID'])
                self.ddp_train(task, model)
            # torchelastic
            elif 'WORLD_SIZE' in os.environ and 'GROUP_RANK' in os.environ:
                task = int(os.environ['LOCAL_RANK'])
                self.ddp_train(task, model)
            else:
                self.__set_random_port()
                # track for predict
                self.model = model
                # train
                mp.spawn(self.ddp_train, nprocs=self.num_processes, args=(model,))
                # load weights if not interrupted
                if self.on_colab_kaggle:
                    self.load_spawn_weights(model)
                    self.model = model

        # 1 gpu or dp option triggers training using DP module
        # easier to avoid NCCL issues
        elif self.use_dp:
            self.dp_train(model)

        elif self.use_horovod:
            self.horovod_train(model)

        elif self.single_gpu:
            self.single_gpu_train(model)

        elif self.use_tpu:  # pragma: no-cover
            log.info(f'training on {self.num_tpu_cores} TPU cores')

            #  COLAB_GPU is an env var available by default in Colab environments.
            start_method = 'fork' if self.on_colab_kaggle else 'spawn'

            # track for predict
            self.model = model

            # train
            xmp.spawn(self.tpu_train, args=(model,), nprocs=self.num_tpu_cores, start_method=start_method)

            # load weights if not interrupted
            self.load_spawn_weights(model)
            self.model = model

        # ON CPU
        else:
            # run through amp wrapper
            if self.use_amp:
                raise MisconfigurationException('amp + cpu is not supported.  Please use a GPU option')

            # CHOOSE OPTIMIZER
            # allow for lr schedulers as well
            self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)

            self.run_pretrain_routine(model)

        # return 1 when finished
        # used for testing or when we need to know that training succeeded
        return 1

    def __set_random_port(self):
        """
        When running DDP NOT managed by SLURM, the ports might collide
        :return:
        """
        try:
            default_port = os.environ['MASTER_PORT']
        except Exception:
            import random
            default_port = random.randint(10000, 19000)
            os.environ['MASTER_PORT'] = str(default_port)

    def __attach_dataloaders(self, model, train_dataloader=None, val_dataloaders=None, test_dataloaders=None):
        # when dataloader is passed via fit, patch the train_dataloader
        # functions to overwrite with these implementations
        if train_dataloader is not None:
            model.train_dataloader = _PatchDataLoader(train_dataloader)

        if val_dataloaders is not None:
            model.val_dataloader = _PatchDataLoader(val_dataloaders)

        if test_dataloaders is not None:
            model.test_dataloader = _PatchDataLoader(test_dataloaders)

    def run_pretrain_routine(self, model: LightningModule):
        """Sanity check a few things before starting actual training.

        Args:
            model: The model to run sanity test on.
        """
        ref_model = model
        if self.data_parallel:
            ref_model = model.module

        # give model convenience properties
        ref_model.trainer = self

        # set local properties on the model
        self.copy_trainer_model_properties(ref_model)

        # init amp. Must be done here instead of __init__ to allow ddp to work
        if self.use_native_amp and self.precision == 16:
            self.scaler = torch.cuda.amp.GradScaler()

        # log hyper-parameters
        if self.logger is not None:
            # save exp to get started
            if hasattr(ref_model, "hparams"):
                self.logger.log_hyperparams(ref_model.hparams)

            self.logger.save()

        if self.use_ddp or self.use_ddp2:
            torch_distrib.barrier()

        # wait for all models to restore weights
        if self.on_tpu and XLA_AVAILABLE:
            # wait for all processes to catch up
            torch_xla.core.xla_model.rendezvous("pl.Trainer.run_pretrain_routine")

        elif self.use_horovod:
            # wait for all processes to catch up
            hvd.join()

        # register auto-resubmit when on SLURM
        self.register_slurm_signal_handlers()

        # print model summary
        # TODO: remove self.testing condition because model.summarize() is wiping out the weights
        if self.proc_rank == 0 and self.weights_summary is not None and not self.testing:
            if self.weights_summary in ['full', 'top']:
                ref_model.summarize(mode=self.weights_summary)
            else:
                raise MisconfigurationException("weights_summary can be None, 'full' or 'top'")

        # track model now.
        # if cluster resets state, the model will update with the saved weights
        self.model = model

        # set up checkpoint callback
        self.configure_checkpoint_callback()

        # restore training and model before hpc call
        self.restore_weights(model)

        # when testing requested only run test and return
        if self.testing:
            # only load test dataloader for testing
            # self.reset_test_dataloader(ref_model)
            self.run_evaluation(test_mode=True)
            return

        # check if we should run validation during training
        self.disable_validation = not (self.is_overridden('validation_step') and self.val_percent_check > 0) \
            and not self.fast_dev_run

        # run tiny validation (if validation defined)
        # to make sure program won't crash during val
        if not self.disable_validation and self.num_sanity_val_steps > 0:
            self.reset_val_dataloader(ref_model)

            # hook and callback
            ref_model.on_sanity_check_start()
            self.on_sanity_check_start()

            eval_results = self._evaluate(model,
                                          self.val_dataloaders,
                                          self.num_sanity_val_steps,
                                          False)
            _, _, _, callback_metrics, _ = self.process_output(eval_results)

            self.on_sanity_check_end()

            # verify that early stop has conditioned on a metric that exists
            if self.enable_early_stop:
                self.early_stop_callback._validate_condition_metric(callback_metrics)

        # clear cache before training
        if self.on_gpu:
            torch.cuda.empty_cache()

        # CORE TRAINING LOOP
        self.train()

    def test(
            self,
            model: Optional[LightningModule] = None,
            test_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None
    ):
        r"""

        Separates from fit to make sure you never run on your test set until you want to.

        Args:
            model: The model to test.

            test_dataloaders: Either a single
                Pytorch Dataloader or a list of them, specifying validation samples.

        Example::

            # Option 1
            # run test after fitting
            test = DataLoader(...)
            trainer = Trainer()
            model = LightningModule()

            trainer.fit(model)
            trainer.test(test_dataloaders=test)

            # Option 2
            # run test from a loaded model
            test = DataLoader(...)
            model = LightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')
            trainer = Trainer()
            trainer.test(model, test_dataloaders=test)
        """

        self.testing = True

        if test_dataloaders is not None:
            if model:
                self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)
            else:
                self.__attach_dataloaders(self.model, test_dataloaders=test_dataloaders)

        # give proper warnings if user only passed in loader without hooks
        self.check_testing_model_configuration(model if model else self.model)

        if model is not None:
            self.model = model
            self.fit(model)
        elif self.use_ddp or self.use_tpu:  # pragma: no-cover
            # attempt to load weights from a spawn
            path = os.path.join(self.default_root_dir, '__temp_weight_ddp_end.ckpt')
            test_model = self.model
            if os.path.exists(path):
                test_model = self.load_spawn_weights(self.model)

            self.fit(test_model)
        else:
            self.run_evaluation(test_mode=True)

        self.testing = False

    def check_model_configuration(self, model: LightningModule):
        r"""
        Checks that the model is configured correctly before training is started.

        Args:
            model: The model to test.

        """
        # Check training_step, train_dataloader, configure_optimizer methods
        if not self.is_overridden('training_step', model):
            raise MisconfigurationException(
                'No `training_step()` method defined. Lightning `Trainer` expects as minimum a'
                ' `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined.')

        if not self.is_overridden('train_dataloader', model):
            raise MisconfigurationException(
                'No `train_dataloader()` method defined. Lightning `Trainer` expects as minimum a'
                ' `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined.')

        if not self.is_overridden('configure_optimizers', model):
            raise MisconfigurationException(
                'No `configure_optimizers()` method defined. Lightning `Trainer` expects as minimum a'
                ' `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined.')

        # Check val_dataloader, validation_step and validation_epoch_end
        if self.is_overridden('val_dataloader', model):
            if not self.is_overridden('validation_step', model):
                raise MisconfigurationException('You have passed in a `val_dataloader()`'
                                                ' but have not defined `validation_step()`.')
            else:
                if not self.is_overridden('validation_epoch_end', model):
                    rank_zero_warn(
                        'You have defined a `val_dataloader()` and have defined a `validation_step()`,'
                        ' you may also want to define `validation_epoch_end()` for accumulating stats.',
                        RuntimeWarning
                    )
        else:
            if self.is_overridden('validation_step', model):
                raise MisconfigurationException('You have defined `validation_step()`,'
                                                ' but have not passed in a val_dataloader().')

        # Check test_dataloader, test_step and test_epoch_end
        if self.is_overridden('test_dataloader', model):
            if not self.is_overridden('test_step', model):
                raise MisconfigurationException('You have passed in a `test_dataloader()`'
                                                ' but have not defined `test_step()`.')
            else:
                if not self.is_overridden('test_epoch_end', model):
                    rank_zero_warn(
                        'You have defined a `test_dataloader()` and have defined a `test_step()`, you may also want to'
                        ' define `test_epoch_end()` for accumulating stats.', RuntimeWarning
                    )

    def check_testing_model_configuration(self, model: LightningModule):

        has_test_step = self.is_overridden('test_step', model)
        has_test_epoch_end = self.is_overridden('test_epoch_end', model)
        gave_test_loader = self.is_overridden('test_dataloader', model)

        if gave_test_loader and not has_test_step:
            raise MisconfigurationException('You passed in a `test_dataloader` but did not implement `test_step()`')

        if has_test_step and not gave_test_loader:
            raise MisconfigurationException('You defined `test_step()` but did not implement'
                                            ' `test_dataloader` nor passed in `.fit(test_dataloaders`.')

        if has_test_step and gave_test_loader and not has_test_epoch_end:
            rank_zero_warn(
                'You passed  in a `test_dataloader` and have defined a `test_step()`, you may also want to'
                ' define `test_epoch_end()` for accumulating stats.', RuntimeWarning
            )


class _PatchDataLoader(object):
    r"""
    Callable object for patching dataloaders passed into trainer.fit().
    Use this class to override model.*_dataloader() and be pickle-compatible.

    Args:
        dataloader: Dataloader object to return when called.

    """

    def __init__(self, dataloader: Union[List[DataLoader], DataLoader]):
        self.dataloader = dataloader

        # cannot pickle __code__ so cannot verify if PatchDataloader
        # exists which shows dataloader methods have been overwritten.
        # so, we hack it by using the string representation
        self.patch_loader_code = str(self.__call__.__code__)

    def __call__(self) -> Union[List[DataLoader], DataLoader]:
        return self.dataloader