trainer.py

import inspect
import math
import os
import re
import shutil
import warnings
from contextlib import contextmanager
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
from packaging import version
from torch import nn
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Dataset
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data.sampler import RandomSampler, Sampler, SequentialSampler
from tqdm.auto import tqdm, trange

from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
from .file_utils import WEIGHTS_NAME, is_datasets_available, is_torch_tpu_available
from .integrations import (
    default_hp_search_backend,
    is_comet_available,
    is_optuna_available,
    is_ray_available,
    is_tensorboard_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
)
from .modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from .modeling_utils import PreTrainedModel
from .optimization import AdamW, get_linear_schedule_with_warmup
from .tokenization_utils_base import PreTrainedTokenizerBase
from .trainer_utils import (
    PREFIX_CHECKPOINT_DIR,
    BestRun,
    EvalPrediction,
    EvaluationStrategy,
    HPSearchBackend,
    PredictionOutput,
    TrainerState,
    TrainOutput,
    default_compute_objective,
    default_hp_space,
    distributed_broadcast_scalars,
    distributed_concat,
    nested_concat,
    nested_detach,
    nested_numpify,
    nested_xla_mesh_reduce,
    set_seed,
)
from .training_args import TrainingArguments
from .utils import logging


_use_native_amp = False
_use_apex = False

PT_LR_SCHEDULER_WARNING = "Please also save or load the state of the optimzer when saving or loading the scheduler."

# Check if Pytorch version >= 1.6 to switch between Native AMP and Apex
if version.parse(torch.__version__) < version.parse("1.6"):
    from .file_utils import is_apex_available

    if is_apex_available():
        from apex import amp
    _use_apex = True
else:
    _use_native_amp = True
    from torch.cuda.amp import autocast

if is_datasets_available():
    import datasets

if is_torch_tpu_available():
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met
    import torch_xla.distributed.parallel_loader as pl

if is_tensorboard_available():
    try:
        from torch.utils.tensorboard import SummaryWriter
    except ImportError:
        from tensorboardX import SummaryWriter

if is_wandb_available():
    import wandb

if is_comet_available():
    import comet_ml

if is_optuna_available():
    import optuna

if is_ray_available():
    from ray import tune

logger = logging.get_logger(__name__)


def reissue_pt_warnings(caught_warnings):
    # Reissue warnings that are not the PT_LR_SCHEDULER_WARNING
    if len(caught_warnings) > 1:
        for w in caught_warnings:
            if w.category != UserWarning or w.message != PT_LR_SCHEDULER_WARNING:
                warnings.warn(w.message, w.category)


@contextmanager
def torch_distributed_zero_first(local_rank: int):
    """
    Decorator to make all processes in distributed training wait for each local_master to do something.

    Args:
        local_rank (:obj:`int`): The rank of the local process.
    """
    if local_rank not in [-1, 0]:
        torch.distributed.barrier()
    yield
    if local_rank == 0:
        torch.distributed.barrier()


class SequentialDistributedSampler(Sampler):
    """
    Distributed Sampler that subsamples indicies sequentially,
    making it easier to collate all results at the end.

    Even though we only use this sampler for eval and predict (no training),
    which means that the model params won't have to be synced (i.e. will not hang
    for synchronization even if varied number of forward passes), we still add extra
    samples to the sampler to make it evenly divisible (like in `DistributedSampler`)
    to make it easy to `gather` or `reduce` resulting tensors at the end of the loop.
    """

    def __init__(self, dataset, num_replicas=None, rank=None):
        if num_replicas is None:
            if not torch.distributed.is_available():
                raise RuntimeError("Requires distributed package to be available")
            num_replicas = torch.distributed.get_world_size()
        if rank is None:
            if not torch.distributed.is_available():
                raise RuntimeError("Requires distributed package to be available")
            rank = torch.distributed.get_rank()
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
        self.total_size = self.num_samples * self.num_replicas

    def __iter__(self):
        indices = list(range(len(self.dataset)))

        # add extra samples to make it evenly divisible
        indices += indices[: (self.total_size - len(indices))]
        assert (
            len(indices) == self.total_size
        ), f"Indices length {len(indices)} and total size {self.total_size} mismatched"

        # subsample
        indices = indices[self.rank * self.num_samples : (self.rank + 1) * self.num_samples]
        assert (
            len(indices) == self.num_samples
        ), f"Indices length {len(indices)} and sample number {self.num_samples} mismatched"

        return iter(indices)

    def __len__(self):
        return self.num_samples


def get_tpu_sampler(dataset: Dataset):
    if xm.xrt_world_size() <= 1:
        return RandomSampler(dataset)
    return DistributedSampler(dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())


class Trainer:
    """
    Trainer is a simple but feature-complete training and eval loop for PyTorch,
    optimized for 🤗 Transformers.

    Args:
        model (:class:`~transformers.PreTrainedModel`, `optional`):
            The model to train, evaluate or use for predictions. If not provided, a ``model_init`` must be passed.
        args (:class:`~transformers.TrainingArguments`, `optional`):
            The arguments to tweak for training. Will default to a basic instance of :class:`~transformers.TrainingArguments`
            with the ``output_dir`` set to a directory named `tmp_trainer` in the current directory if not provided.
        data_collator (:obj:`DataCollator`, `optional`):
            The function to use to form a batch from a list of elements of :obj:`train_dataset` or
            :obj:`eval_dataset`. Will default to :func:`~transformers.default_data_collator` if no ``tokenizer`` is
            provided, an instance of :func:`~transformers.DataCollatorWithPadding` otherwise.
        train_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
            The dataset to use for training. If it is an :obj:`datasets.Dataset`, columns not accepted by the
            ``model.forward()`` method are automatically removed.
        eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
             The dataset to use for evaluation. If it is an :obj:`datasets.Dataset`, columns not accepted by the
            ``model.forward()`` method are automatically removed.
        tokenizer (:class:`PreTrainedTokenizerBase`, `optional`):
            The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs the
            maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
            interrupted training or reuse the fine-tuned model.
        model_init (:obj:`Callable[[], PreTrainedModel]`, `optional`):
            A function that instantiates the model to be used. If provided, each call to
            :meth:`~transformers.Trainer.train` will start from a new instance of the model as given by this function.
        compute_metrics (:obj:`Callable[[EvalPrediction], Dict]`, `optional`):
            The function that will be used to compute metrics at evaluation. Must take a
            :class:`~transformers.EvalPrediction` and return a dictionary string to metric values.
        tb_writer (:obj:`SummaryWriter`, `optional`):
            Object to write to TensorBoard.
        optimizers (:obj:`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR`, `optional`):
            A tuple containing the optimizer and the scheduler to use. Will default to an instance of
            :class:`~transformers.AdamW` on your model and a scheduler given by
            :func:`~transformers.get_linear_schedule_with_warmup` controlled by :obj:`args`.
        kwargs:
            Deprecated keyword arguments.
    """

    def __init__(
        self,
        model: PreTrainedModel = None,
        args: TrainingArguments = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Dataset] = None,
        tokenizer: Optional["PreTrainedTokenizerBase"] = None,
        model_init: Callable[[], PreTrainedModel] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
        tb_writer: Optional["SummaryWriter"] = None,
        optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        **kwargs,
    ):
        if args is None:
            logger.info("No `TrainingArguments` passed, using the current path as `output_dir`.")
            args = TrainingArguments("tmp_trainer")
        self.args = args
        # Seed must be set before instantiating the model when using model
        set_seed(self.args.seed)
        assert (
            model is not None or model_init is not None
        ), "You must provide a model to use `Trainer`, either by using the `model` argument or the `model_init` argument."
        if model is None and model_init is not None:
            model = model_init()
        self.model = model.to(args.device) if model is not None else None
        default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer)
        self.data_collator = data_collator if data_collator is not None else default_collator
        self.train_dataset = train_dataset
        self.eval_dataset = eval_dataset
        self.tokenizer = tokenizer
        self.model_init = model_init
        self.compute_metrics = compute_metrics
        self.optimizer, self.lr_scheduler = optimizers
        if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None):
            raise RuntimeError(
                "Passing a `model_init` is incompatible with providing the `optimizers` argument."
                "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
            )
        self.tb_writer = tb_writer
        if "prediction_loss_only" in kwargs:
            warnings.warn(
                "Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a "
                + "future version. Use `args.prediction_loss_only` instead. Setting "
                + f"`args.prediction_loss_only={kwargs['prediction_loss_only']}",
                FutureWarning,
            )
            self.args.prediction_loss_only = kwargs.pop("prediction_loss_only")
        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

        if tb_writer is None and is_tensorboard_available() and self.is_world_process_zero():
            self.tb_writer = SummaryWriter(log_dir=self.args.logging_dir)
        if not is_tensorboard_available():
            logger.warning(
                "You are instantiating a Trainer but Tensorboard is not installed. You should consider installing it."
            )

        # Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
        self._loggers_initialized = False

        # Create output directory if needed
        if self.is_world_process_zero():
            os.makedirs(self.args.output_dir, exist_ok=True)
        if is_torch_tpu_available() and isinstance(self.model, PreTrainedModel):
            # Set an xla_device flag on the model's config.
            # We'll find a more elegant and not need to do this in the future.
            self.model.config.xla_device = True
        if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
            self.data_collator = self.data_collator.collate_batch
            warnings.warn(
                (
                    "The `data_collator` should now be a simple callable (function, class with `__call__`), classes "
                    + "with a `collate_batch` are deprecated and won't be supported in a future version."
                ),
                FutureWarning,
            )

        if is_datasets_available():
            if isinstance(train_dataset, datasets.Dataset):
                self._remove_unused_columns(self.train_dataset, description="training")
            if isinstance(eval_dataset, datasets.Dataset):
                self._remove_unused_columns(self.eval_dataset, description="evaluation")

        self.state = TrainerState()
        # Internal variable for total_flos used to count as tensors (for distributed + TPU), will be sent in the
        # state at each call to self.log.
        self._total_flos = None
        if self.args.fp16 and _use_native_amp:
            self.scaler = torch.cuda.amp.GradScaler()
        self.hp_search_backend = None
        self.use_tune_checkpoints = False
        default_label_names = (
            ["start_positions, end_positions"]
            if type(self.model) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values()
            else ["labels"]
        )
        self.label_names = default_label_names if self.args.label_names is None else self.args.label_names

    def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
        if not self.args.remove_unused_columns:
            return
        # Inspect model forward signature to keep only the arguments it accepts.
        signature = inspect.signature(self.model.forward)
        signature_columns = list(signature.parameters.keys())
        # Labels may be named label or label_ids, the default data collator handles that.
        signature_columns += ["label", "label_ids"]
        columns = [k for k in signature_columns if k in dataset.column_names]
        ignored_columns = list(set(dataset.column_names) - set(signature_columns))
        dset_description = "" if description is None else f"in the {description} set "
        logger.info(
            f"The following columns {dset_description}don't have a corresponding argument in `{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}."
        )
        dataset.set_format(type=dataset.format["type"], columns=columns)

    def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
        if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
            return None
        elif is_torch_tpu_available():
            return get_tpu_sampler(self.train_dataset)
        else:
            return (
                RandomSampler(self.train_dataset)
                if self.args.local_rank == -1
                else DistributedSampler(self.train_dataset)
            )

    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training :class:`~torch.utils.data.DataLoader`.

        Will use no sampler if :obj:`self.train_dataset` is a :obj:`torch.utils.data.IterableDataset`, a random sampler
        (adapted to distributed training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.
        """
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")
        train_sampler = self._get_train_sampler()

        return DataLoader(
            self.train_dataset,
            batch_size=self.args.train_batch_size,
            sampler=train_sampler,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
        )

    def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.sampler.Sampler]:
        if isinstance(eval_dataset, torch.utils.data.IterableDataset):
            return None
        elif is_torch_tpu_available():
            return SequentialDistributedSampler(eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
        elif self.args.local_rank != -1:
            return SequentialDistributedSampler(eval_dataset)
        else:
            return SequentialSampler(eval_dataset)

    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
        """
        Returns the evaluation :class:`~torch.utils.data.DataLoader`.

        Will use no sampler if :obj:`self.eval_dataset` is a :obj:`torch.utils.data.IterableDataset`, a sequential
        sampler (adapted to distributed training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
                If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not
                accepted by the ``model.forward()`` method are automatically removed.
        """
        if eval_dataset is None and self.eval_dataset is None:
            raise ValueError("Trainer: evaluation requires an eval_dataset.")
        elif eval_dataset is not None and is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
            self._remove_unused_columns(eval_dataset, description="evaluation")
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        eval_sampler = self._get_eval_sampler(eval_dataset)

        return DataLoader(
            eval_dataset,
            sampler=eval_sampler,
            batch_size=self.args.eval_batch_size,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
        )

    def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
        """
        Returns the test :class:`~torch.utils.data.DataLoader`.

        Will use no sampler if :obj:`test_dataset` is a :obj:`torch.utils.data.IterableDataset`, a sequential
        sampler (adapted to distributed training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
                The test dataset to use. If it is an :obj:`datasets.Dataset`, columns not accepted by the
                ``model.forward()`` method are automatically removed.
        """
        if is_datasets_available() and isinstance(test_dataset, datasets.Dataset):
            self._remove_unused_columns(test_dataset, description="test")
        test_sampler = self._get_eval_sampler(test_dataset)

        # We use the same batch_size as for eval.
        return DataLoader(
            test_dataset,
            sampler=test_sampler,
            batch_size=self.args.eval_batch_size,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
        )

    def create_optimizer_and_scheduler(self, num_training_steps: int):
        """
        Setup the optimizer and the learning rate scheduler.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
        """
        if self.optimizer is None:
            no_decay = ["bias", "LayerNorm.weight"]
            optimizer_grouped_parameters = [
                {
                    "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
                    "weight_decay": 0.0,
                },
            ]
            self.optimizer = AdamW(
                optimizer_grouped_parameters,
                lr=self.args.learning_rate,
                betas=(self.args.adam_beta1, self.args.adam_beta2),
                eps=self.args.adam_epsilon,
            )
        if self.lr_scheduler is None:
            self.lr_scheduler = get_linear_schedule_with_warmup(
                self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
            )

    def setup_wandb(self):
        """
        Setup the optional Weights & Biases (`wandb`) integration.

        One can subclass and override this method to customize the setup if needed. Find more information
        `here <https://docs.wandb.com/huggingface>`__. You can also override the following environment variables:

        Environment:
            WANDB_WATCH:
                (Optional, ["gradients", "all", "false"]) "gradients" by default, set to "false" to disable gradient logging
                or "all" to log gradients and parameters
            WANDB_PROJECT:
                (Optional): str - "huggingface" by default, set this to a custom string to store results in a different project
            WANDB_DISABLED:
                (Optional): boolean - defaults to false, set to "true" to disable wandb entirely
        """
        if hasattr(self, "_setup_wandb"):
            warnings.warn(
                "The `_setup_wandb` method is deprecated and won't be called in a future version, define `setup_wandb` in your subclass.",
                FutureWarning,
            )
            return self._setup_wandb()

        if self.is_world_process_zero():
            logger.info(
                'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"'
            )
            combined_dict = {**self.args.to_sanitized_dict()}
            if isinstance(self.model, PreTrainedModel):
                combined_dict = {**self.model.config.to_dict(), **combined_dict}
            wandb.init(
                project=os.getenv("WANDB_PROJECT", "huggingface"), config=combined_dict, name=self.args.run_name
            )
            # keep track of model topology and gradients, unsupported on TPU
            if not is_torch_tpu_available() and os.getenv("WANDB_WATCH") != "false":
                wandb.watch(
                    self.model, log=os.getenv("WANDB_WATCH", "gradients"), log_freq=max(100, self.args.logging_steps)
                )

    def setup_comet(self):
        """
        Setup the optional Comet.ml integration.

        Environment:
            COMET_MODE:
                (Optional): str - "OFFLINE", "ONLINE", or "DISABLED"
            COMET_PROJECT_NAME:
                (Optional): str - Comet.ml project name for experiments
            COMET_OFFLINE_DIRECTORY:
                (Optional): str - folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE"

        For a number of configurable items in the environment,
        see `here <https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables>`__
        """
        if self.is_world_master():
            comet_mode = os.getenv("COMET_MODE", "ONLINE").upper()
            args = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")}
            experiment = None
            if comet_mode == "ONLINE":
                experiment = comet_ml.Experiment(**args)
                logger.info("Automatic Comet.ml online logging enabled")
            elif comet_mode == "OFFLINE":
                args["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./")
                experiment = comet_ml.OfflineExperiment(**args)
                logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished")
            if experiment is not None:
                experiment._set_model_graph(self.model, framework="transformers")
                experiment._log_parameters(self.args, prefix="args/", framework="transformers")
                if isinstance(self.model, PreTrainedModel):
                    experiment._log_parameters(self.model.config, prefix="config/", framework="transformers")

    def num_examples(self, dataloader: DataLoader) -> int:
        """
        Helper to get number of samples in a :class:`~torch.utils.data.DataLoader` by accessing its dataset.
        """
        return len(dataloader.dataset)

    def _setup_loggers(self):
        if self._loggers_initialized:
            return
        if is_wandb_available():
            self.setup_wandb()
        elif os.environ.get("WANDB_DISABLED") != "true":
            logger.info(
                "You are instantiating a Trainer but W&B is not installed. To use wandb logging, "
                "run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface."
            )
        if is_comet_available():
            self.setup_comet()
        elif os.environ.get("COMET_MODE") != "DISABLED":
            logger.info(
                "To use comet_ml logging, run `pip/conda install comet_ml` "
                "see https://www.comet.ml/docs/python-sdk/huggingface/"
            )
        self._loggers_initialized = True

    def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
        """ HP search setup code """
        if self.hp_search_backend is None or trial is None:
            return
        params = self.hp_space(trial) if self.hp_search_backend == HPSearchBackend.OPTUNA else trial
        for key, value in params.items():
            if not hasattr(self.args, key):
                raise AttributeError(
                    f"Trying to set {key} in the hyperparameter search but there is no corresponding field in `TrainingArguments`."
                )
            old_attr = getattr(self.args, key, None)
            # Casting value to the proper type
            if old_attr is not None:
                value = type(old_attr)(value)
            setattr(self.args, key, value)
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            logger.info("Trial:", trial.params)

    def _report_to_hp_search(
        self, trial: Union["optuna.Trial", Dict[str, Any]], epoch: int, metrics: Dict[str, float]
    ):
        if self.hp_search_backend is None or trial is None:
            return
        self.objective = self.compute_objective(metrics)
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            trial.report(self.objective, epoch)
            if trial.should_prune():
                raise optuna.TrialPruned()
        elif self.hp_search_backend == HPSearchBackend.RAY:
            if self.state.global_step % self.args.save_steps == 0:
                self._tune_save_checkpoint()
            tune.report(objective=self.objective, **metrics)

    def _tune_save_checkpoint(self):
        if not self.use_tune_checkpoints:
            return
        with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
            self.args.output_dir = checkpoint_dir
            output_dir = os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
            self.save_model(output_dir)
            if self.is_world_master():
                self.state.save_to_json(os.path.join(output_dir, "trainer_state.json"))
                torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))

    def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", Dict[str, Any]] = None):
        """
        Main training entry point.

        Args:
            model_path (:obj:`str`, `optional`):
                Local path to the model if the model to train has been instantiated from a local path. If present,
                training will resume from the optimizer/scheduler states loaded here.
            trial (:obj:`optuna.Trial` or :obj:`Dict[str, Any]`, `optional`):
                The trial run or the hyperparameter dictionary for hyperparameter search.
        """
        # This might change the seed so needs to run first.
        self._hp_search_setup(trial)

        # Model re-init
        if self.model_init is not None:
            # Seed must be set before instantiating the model when using model_init.
            set_seed(self.args.seed)
            model = self.model_init()
            self.model = model.to(self.args.device)

            # Reinitializes optimizer and scheduler
            self.optimizer, self.lr_scheduler = None, None

        # Data loader and number of training steps
        train_dataloader = self.get_train_dataloader()
        num_update_steps_per_epoch = len(train_dataloader) // self.args.gradient_accumulation_steps
        num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
        if self.args.max_steps > 0:
            max_steps = self.args.max_steps
            num_train_epochs = self.args.max_steps // num_update_steps_per_epoch + int(
                self.args.max_steps % num_update_steps_per_epoch > 0
            )
        else:
            max_steps = int(num_update_steps_per_epoch * self.args.num_train_epochs)
            num_train_epochs = self.args.num_train_epochs
        num_train_epochs = int(np.ceil(num_train_epochs))

        self.create_optimizer_and_scheduler(num_training_steps=max_steps)
        self.state = TrainerState()

        # Check if saved optimizer or scheduler states exist
        if (
            model_path is not None
            and os.path.isfile(os.path.join(model_path, "optimizer.pt"))
            and os.path.isfile(os.path.join(model_path, "scheduler.pt"))
        ):
            # Load in optimizer and scheduler states
            self.optimizer.load_state_dict(
                torch.load(os.path.join(model_path, "optimizer.pt"), map_location=self.args.device)
            )
            with warnings.catch_warnings(record=True) as caught_warnings:
                self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
            reissue_pt_warnings(caught_warnings)

        # Moxed precision training with apex (torch < 1.6)
        model = self.model
        if self.args.fp16 and _use_apex:
            if not is_apex_available():
                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
            model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)

        # Multi-gpu training (should be after apex fp16 initialization)
        if self.args.n_gpu > 1:
            model = torch.nn.DataParallel(model)

        # Distributed training (should be after apex fp16 initialization)
        if self.args.local_rank != -1:
            model = torch.nn.parallel.DistributedDataParallel(
                model,
                device_ids=[self.args.local_rank],
                output_device=self.args.local_rank,
                find_unused_parameters=(
                    not getattr(model.config, "gradient_checkpointing", False)
                    if isinstance(model, PreTrainedModel)
                    else True
                ),
            )
        # find_unused_parameters breaks checkpointing as per
        # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021

        if self.tb_writer is not None:
            self.tb_writer.add_text("args", self.args.to_json_string())
            self.tb_writer.add_hparams(self.args.to_sanitized_dict(), metric_dict={})

        # Train!
        if is_torch_tpu_available():
            total_train_batch_size = self.args.train_batch_size * xm.xrt_world_size()
        else:
            total_train_batch_size = (
                self.args.train_batch_size
                * self.args.gradient_accumulation_steps
                * (torch.distributed.get_world_size() if self.args.local_rank != -1 else 1)
            )
        logger.info("***** Running training *****")
        logger.info("  Num examples = %d", self.num_examples(train_dataloader))
        logger.info("  Num Epochs = %d", num_train_epochs)
        logger.info("  Instantaneous batch size per device = %d", self.args.per_device_train_batch_size)
        logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d", total_train_batch_size)
        logger.info("  Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps)
        logger.info("  Total optimization steps = %d", max_steps)

        self.state.epoch = 0
        epochs_trained = 0
        steps_trained_in_current_epoch = 0

        # Check if continuing training from a checkpoint
        if model_path and os.path.isfile(os.path.join(model_path, "trainer_state.json")):
            self.state = TrainerState.load_from_json(os.path.join(model_path, "trainer_state.json"))
            epochs_trained = self.state.global_step // num_update_steps_per_epoch
            steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)

            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
            logger.info("  Continuing training from epoch %d", epochs_trained)
            logger.info("  Continuing training from global step %d", self.state.global_step)
            logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)

        # This should be the same if the state has been saved but in case the training arguments changed, it's safer
        # to set this after the load.
        self.state.max_steps = max_steps
        self.state.num_train_epochs = num_train_epochs

        tr_loss = torch.tensor(0.0).to(self.args.device)
        self._total_flos = self.state.total_flos
        logging_loss_scalar = 0.0
        model.zero_grad()
        disable_tqdm = self.args.disable_tqdm or not self.is_local_process_zero()
        train_pbar = trange(epochs_trained, num_train_epochs, desc="Epoch", disable=disable_tqdm)
        for epoch in range(epochs_trained, num_train_epochs):
            if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                train_dataloader.sampler.set_epoch(epoch)

            if is_torch_tpu_available():
                parallel_loader = pl.ParallelLoader(train_dataloader, [self.args.device]).per_device_loader(
                    self.args.device
                )
                epoch_iterator = parallel_loader
            else:
                epoch_iterator = train_dataloader

            # Reset the past mems state at the beginning of each epoch if necessary.
            if self.args.past_index >= 0:
                self._past = None

            epoch_pbar = tqdm(epoch_iterator, desc="Iteration", disable=disable_tqdm)
            for step, inputs in enumerate(epoch_iterator):

                # Skip past any already trained steps if resuming training
                if steps_trained_in_current_epoch > 0:
                    steps_trained_in_current_epoch -= 1
                    epoch_pbar.update(1)
                    continue

                tr_loss += self.training_step(model, inputs)
                self._total_flos += self.floating_point_ops(inputs)

                if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
                    # last step in epoch but step is always smaller than gradient_accumulation_steps
                    len(epoch_iterator) <= self.args.gradient_accumulation_steps
                    and (step + 1) == len(epoch_iterator)
                ):
                    if self.args.fp16 and _use_native_amp:
                        self.scaler.unscale_(self.optimizer)
                        torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm)
                    elif self.args.fp16 and _use_apex:
                        torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.args.max_grad_norm)
                    else:
                        torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm)

                    if is_torch_tpu_available():
                        xm.optimizer_step(self.optimizer)
                    elif self.args.fp16 and _use_native_amp:
                        self.scaler.step(self.optimizer)
                        self.scaler.update()
                    else:
                        self.optimizer.step()

                    self.lr_scheduler.step()
                    model.zero_grad()
                    self.state.global_step += 1
                    self.state.epoch = epoch + (step + 1) / len(epoch_iterator)

                    if (self.args.logging_steps > 0 and self.state.global_step % self.args.logging_steps == 0) or (
                        self.state.global_step == 1 and self.args.logging_first_step
                    ):
                        logs: Dict[str, float] = {}
                        tr_loss_scalar = tr_loss.item()
                        logs["loss"] = (tr_loss_scalar - logging_loss_scalar) / self.args.logging_steps
                        # backward compatibility for pytorch schedulers
                        logs["learning_rate"] = (
                            self.lr_scheduler.get_last_lr()[0]
                            if version.parse(torch.__version__) >= version.parse("1.4")
                            else self.lr_scheduler.get_lr()[0]
                        )
                        logging_loss_scalar = tr_loss_scalar

                        self.log(logs)

                    if (
                        self.args.evaluation_strategy == EvaluationStrategy.STEPS
                        and self.state.global_step % self.args.eval_steps == 0
                    ):
                        metrics = self.evaluate()
                        self._report_to_hp_search(trial, epoch, metrics)
                        if self.args.load_best_model_at_end:
                            self._save_training(model, trial, metrics=metrics)

                    if (
                        not self.args.load_best_model_at_end
                        and self.args.save_steps > 0
                        and self.state.global_step % self.args.save_steps == 0
                    ):
                        self._save_training(model, trial)

                epoch_pbar.update(1)
                if self.state.global_step >= max_steps:
                    break
            epoch_pbar.close()
            train_pbar.update(1)

            if self.args.evaluation_strategy == EvaluationStrategy.EPOCH:
                metrics = self.evaluate()
                self._report_to_hp_search(trial, epoch, metrics)
                if self.args.load_best_model_at_end:
                    self._save_training(model, trial, metrics=metrics)

            if self.args.tpu_metrics_debug or self.args.debug:
                if is_torch_tpu_available():
                    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                    xm.master_print(met.metrics_report())
                else:
                    logger.warning(
                        "You enabled PyTorch/XLA debug metrics but you don't have a TPU "
                        "configured. Check your training configuration if this is unexpected."
                    )
            if self.state.global_step >= max_steps:
                break

        train_pbar.close()
        if self.tb_writer:
            self.tb_writer.close()
        if self.args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of training
            delattr(self, "_past")

        logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
        if self.args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
            logger.info(
                f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
            )
            if isinstance(model, PreTrainedModel):
                self.model = model.from_pretrained(self.state.best_model_checkpoint)
                self.model = self.model.to(self.args.device)
            else:
                state_dict = torch.load(os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME))
                self.model.load_state_dict(state_dict)

        return TrainOutput(self.state.global_step, tr_loss.item() / self.state.global_step)

    def _save_training(self, model, trial, metrics=None):
        # In all cases (even distributed/parallel), self.model is always a reference
        # to the model we want to save.
        if hasattr(model, "module"):
            assert model.module is self.model, f"Module {model.module} should be a reference to self.model"
        else:
            assert model is self.model, f"Model {model} should be a reference to self.model"
        # Save model checkpoint
        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
        if self.hp_search_backend is not None and trial is not None:
            run_id = trial.number if self.hp_search_backend == HPSearchBackend.OPTUNA else tune.get_trial_id()
            checkpoint_folder += f"-run-{run_id}"
        output_dir = os.path.join(self.args.output_dir, checkpoint_folder)

        self.store_flos()
        self.save_model(output_dir)

        # Save optimizer and scheduler
        if is_torch_tpu_available():
            xm.rendezvous("saving_optimizer_states")
            xm.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
            with warnings.catch_warnings(record=True) as caught_warnings:
                xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
                reissue_pt_warnings(caught_warnings)
        elif self.is_world_process_zero():
            torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
            with warnings.catch_warnings(record=True) as caught_warnings:
                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
            reissue_pt_warnings(caught_warnings)

        # Determine the new best metric / best model checkpoint
        if metrics is not None:
            metric_to_check = self.args.metric_for_best_model
            if not metric_to_check.startswith("eval_"):
                metric_to_check = f"eval_{metric_to_check}"
            metric_value = metrics[metric_to_check]

            operator = np.greater if self.args.greater_is_better else np.less
            if (
                self.state.best_metric is None
                or self.state.best_model_checkpoint is None
                or operator(metric_value, self.state.best_metric)
            ):
                self.state.best_metric = metric_value
                self.state.best_model_checkpoint = output_dir

        # Save the Trainer state
        if self.is_world_process_zero():
            self.state.save_to_json(os.path.join(output_dir, "trainer_state.json"))

        # Maybe delete some older checkpoints.
        if self.is_world_process_zero():
            self._rotate_checkpoints(use_mtime=True)

    def hyperparameter_search(
        self,
        hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
        compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
        n_trials: int = 20,
        direction: str = "minimize",
        backend: Optional[Union["str", HPSearchBackend]] = None,
        **kwargs
    ) -> BestRun:
        """
        Launch an hyperparameter search using ``optuna`` or ``Ray Tune``. The optimized quantity is determined by
        :obj:`compute_objectie`, which defaults to a function returning the evaluation loss when no metric is provided,
        the sum of all metrics otherwise.

        .. warning::

            To use this method, you need to have provided a ``model_init`` when initializing your
            :class:`~transformers.Trainer`: we need to reinitialize the model at each new run. This is incompatible
            with the ``optimizers`` argument, so you need to subclass :class:`~transformers.Trainer` and override the
            method :meth:`~transformers.Trainer.create_optimizer_and_scheduler` for custom optimizer/scheduler.

        Args:
            hp_space (:obj:`Callable[["optuna.Trial"], Dict[str, float]]`, `optional`):
                A function that defines the hyperparameter search space. Will default to
                :func:`~transformers.trainer_utils.default_hp_space_optuna` or
                :func:`~transformers.trainer_utils.default_hp_space_ray` depending on your backend.
            compute_objective (:obj:`Callable[[Dict[str, float]], float]`, `optional`):
                A function computing the objective to minimize or maximize from the metrics returned by the
                :obj:`evaluate` method. Will default to :func:`~transformers.trainer_utils.default_compute_objective`.
            n_trials (:obj:`int`, `optional`, defaults to 100):
                The number of trial runs to test.
            direction(:obj:`str`, `optional`, defaults to :obj:`"minimize"`):
                Whether to optimize greater or lower objects. Can be :obj:`"minimize"` or :obj:`"maximize"`, you should
                pick :obj:`"minimize"` when optimizing the validation loss, :obj:`"maximize"` when optimizing one or
                several metrics.
            backend(:obj:`str` or :class:`~transformers.training_utils.HPSearchBackend`, `optional`):
                The backend to use for hyperparameter search. Will default to optuna or Ray Tune, depending on which
                one is installed. If both are installed, will default to optuna.
            kwargs:
                Additional keyword arguments passed along to :obj:`optuna.create_study` or :obj:`ray.tune.run`. For
                more information see:

                - the documentation of `optuna.create_study <https://optuna.readthedocs.io/en/stable/reference/alias_generated/optuna.create_study.html#optuna.create_study>`__
                - the documentation of `tune.run <https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run>`__

        Returns:
            :class:`transformers.trainer_utils.BestRun`: All the informations about the best run.
        """
        if backend is None:
            backend = default_hp_search_backend()
            if backend is None:
                raise RuntimeError(
                    "At least one of optuna or ray should be installed. "
                    "To install optuna run `pip install optuna`."
                    "To install ray run `pip install ray[tune]`."
                )
        backend = HPSearchBackend(backend)
        if backend == HPSearchBackend.OPTUNA and not is_optuna_available():
            raise RuntimeError("You picked the optuna backend, but it is not installed. Use `pip install optuna`.")
        if backend == HPSearchBackend.RAY and not is_ray_available():
            raise RuntimeError(
                "You picked the Ray Tune backend, but it is not installed. Use `pip install 'ray[tune]'`."
            )
        self.hp_search_backend = backend

        if self.model_init is None:
            raise RuntimeError(
                "To use hyperparameter search, you need to pass your model through a model_init function."
            )

        self.hp_space = default_hp_space[backend] if hp_space is None else hp_space
        self.compute_objective = default_compute_objective if compute_objective is None else compute_objective

        run_hp_search = run_hp_search_optuna if backend == HPSearchBackend.OPTUNA else run_hp_search_ray
        best_run = run_hp_search(self, n_trials, direction, **kwargs)

        self.hp_search_backend = None
        return best_run

    def log(self, logs: Dict[str, float], iterator: Optional[tqdm] = None) -> None:
        """
        Log :obj:`logs` on the various objects watching training.

        Subclass and override this method to inject custom behavior.

        Args:
            logs (:obj:`Dict[str, float]`):
                The values to log.
            iterator (:obj:`tqdm`, `optional`):
                A potential tqdm progress bar to write the logs on.
        """
        # Set up loggers like W&B or Comet ML
        self._setup_loggers()

        if hasattr(self, "_log"):
            warnings.warn(
                "The `_log` method is deprecated and won't be called in a future version, define `log` in your subclass.",
                FutureWarning,
            )
            return self._log(logs, iterator=iterator)

        if self.state.epoch is not None:
            logs["epoch"] = self.state.epoch
        if self._total_flos is not None:
            self.store_flos()
            logs["total_flos"] = self.state.total_flos
        if self.tb_writer:
            for k, v in logs.items():
                if isinstance(v, (int, float)):
                    self.tb_writer.add_scalar(k, v, self.state.global_step)
                else:
                    logger.warning(
                        "Trainer is attempting to log a value of "
                        '"%s" of type %s for key "%s" as a scalar. '
                        "This invocation of Tensorboard's writer.add_scalar() "
                        "is incorrect so we dropped this attribute.",
                        v,
                        type(v),
                        k,
                    )
            self.tb_writer.flush()
        if is_wandb_available():
            if self.is_world_process_zero():
                wandb.log(logs, step=self.state.global_step)
        if is_comet_available():
            if self.is_world_process_zero():
                experiment = comet_ml.config.get_global_experiment()
                if experiment is not None:
                    experiment._log_metrics(
                        logs, step=self.state.global_step, epoch=self.state.epoch, framework="transformers"
                    )
        output = {**logs, **{"step": self.state.global_step}}
        self.state.log_history.append(output)
        if iterator is not None:
            iterator.write(output)
        else:
            print(output)

    def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
        """
        Prepare :obj:`inputs` before feeding them to the model, converting them to tensors if they are not already and
        handling potential state.
        """
        for k, v in inputs.items():
            if isinstance(v, torch.Tensor):
                inputs[k] = v.to(self.args.device)

        if self.args.past_index >= 0 and self._past is not None:
            inputs["mems"] = self._past

        return inputs

    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
        """
        Perform a training step on a batch of inputs.

        Subclass and override to inject custom behavior.

        Args:
            model (:obj:`nn.Module`):
                The model to train.
            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument :obj:`labels`. Check your model's documentation for all accepted arguments.

        Return:
            :obj:`torch.Tensor`: The tensor with training loss on this batch.
        """
        if hasattr(self, "_training_step"):
            warnings.warn(
                "The `_training_step` method is deprecated and won't be called in a future version, define `training_step` in your subclass.",
                FutureWarning,
            )
            return self._training_step(model, inputs, self.optimizer)

        model.train()
        inputs = self._prepare_inputs(inputs)

        if self.args.fp16 and _use_native_amp:
            with autocast():
                loss = self.compute_loss(model, inputs)
        else:
            loss = self.compute_loss(model, inputs)

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training

        if self.args.gradient_accumulation_steps > 1:
            loss = loss / self.args.gradient_accumulation_steps

        if self.args.fp16 and _use_native_amp:
            self.scaler.scale(loss).backward()
        elif self.args.fp16 and _use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()

        return loss.detach()

    def compute_loss(self, model, inputs):
        """
        How the loss is computed by Trainer. By default, all models return the loss in the first element.

        Subclass and override for custom behavior.
        """
        outputs = model(**inputs)
        # Save past state if it exists
        if self.args.past_index >= 0:
            self._past = outputs[self.args.past_index]
        # We don't use .loss here since the model may return tuples instead of ModelOutput.
        return outputs[0]

    def is_local_master(self) -> bool:
        """
        Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on
        several machines) main process.

        .. warning::

            This method is deprecated, use :meth:`~transformers.Trainer.is_local_process_zero` instead.
        """
        warnings.warn("This method is deprecated, use `Trainer.is_local_process_zero()` instead.", FutureWarning)
        return self.is_local_process_zero()

    def is_local_process_zero(self) -> bool:
        """
        Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on
        several machines) main process.
        """
        if is_torch_tpu_available():
            return xm.is_master_ordinal(local=True)
        else:
            return self.args.local_rank in [-1, 0]

    def is_world_master(self) -> bool:
        """
        Whether or not this process is the global main process (when training in a distributed fashion on
        several machines, this is only going to be :obj:`True` for one process).

        .. warning::

            This method is deprecated, use :meth:`~transformers.Trainer.is_world_process_zero` instead.
        """
        warnings.warn("This method is deprecated, use `Trainer.is_world_process_zero()` instead.", FutureWarning)
        return self.is_world_process_zero()

    def is_world_process_zero(self) -> bool:
        """
        Whether or not this process is the global main process (when training in a distributed fashion on
        several machines, this is only going to be :obj:`True` for one process).
        """
        if is_torch_tpu_available():
            return xm.is_master_ordinal(local=False)
        else:
            return self.args.local_rank == -1 or torch.distributed.get_rank() == 0

    def save_model(self, output_dir: Optional[str] = None):
        """
        Will save the model, so you can reload it using :obj:`from_pretrained()`.

        Will only save from the world_master process (unless in TPUs).
        """

        if is_torch_tpu_available():
            self._save_tpu(output_dir)
        elif self.is_world_process_zero():
            self._save(output_dir)

    def _save_tpu(self, output_dir: Optional[str] = None):
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        logger.info("Saving model checkpoint to %s", output_dir)

        if xm.is_master_ordinal():
            os.makedirs(output_dir, exist_ok=True)
            torch.save(self.args, os.path.join(output_dir, "training_args.bin"))

        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        xm.rendezvous("saving_checkpoint")
        if not isinstance(self.model, PreTrainedModel):
            logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
            state_dict = self.model.state_dict()
            xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            self.model.save_pretrained(output_dir)
        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)

    def _save(self, output_dir: Optional[str] = None):
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        os.makedirs(output_dir, exist_ok=True)
        logger.info("Saving model checkpoint to %s", output_dir)
        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        if not isinstance(self.model, PreTrainedModel):
            logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
            state_dict = self.model.state_dict()
            torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            self.model.save_pretrained(output_dir)
        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(self.args, os.path.join(output_dir, "training_args.bin"))

    def store_flos(self):
        # Storing the number of floating-point operations that went into the model
        if self._total_flos is not None:
            if self.args.local_rank != -1:
                self.state.total_flos = distributed_broadcast_scalars([self._total_flos]).sum().item()
            else:
                self.state.total_flos = self._total_flos

    def _sorted_checkpoints(self, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False) -> List[str]:
        ordering_and_checkpoint_path = []

        glob_checkpoints = [str(x) for x in Path(self.args.output_dir).glob(f"{checkpoint_prefix}-*")]

        for path in glob_checkpoints:
            if use_mtime:
                ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
            else:
                regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
                if regex_match and regex_match.groups():
                    ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))

        checkpoints_sorted = sorted(ordering_and_checkpoint_path)
        checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
        # Make sure we don't delete the best model.
        if self.state.best_model_checkpoint is not None:
            best_model_index = checkpoints_sorted.index(self.state.best_model_checkpoint)
            checkpoints_sorted[best_model_index], checkpoints_sorted[best_model_index][-1] = (
                checkpoints_sorted[-1],
                checkpoints_sorted[best_model_index],
            )
        return checkpoints_sorted

    def _rotate_checkpoints(self, use_mtime=False) -> None:
        if self.args.save_total_limit is None or self.args.save_total_limit <= 0:
            return

        # Check if we should delete older checkpoint(s)
        checkpoints_sorted = self._sorted_checkpoints(use_mtime=use_mtime)
        if len(checkpoints_sorted) <= self.args.save_total_limit:
            return

        number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - self.args.save_total_limit)
        checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
        for checkpoint in checkpoints_to_be_deleted:
            logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
            shutil.rmtree(checkpoint)

    def evaluate(self, eval_dataset: Optional[Dataset] = None) -> Dict[str, float]:
        """
        Run evaluation and returns metrics.

        The calling script will be responsible for providing a method to compute metrics, as they are
        task-dependent (pass it to the init :obj:`compute_metrics` argument).

        You can also subclass and override this method to inject custom behavior.

        Args:
            eval_dataset (:obj:`Dataset`, `optional`):
                Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`,
                columns not accepted by the ``model.forward()`` method are automatically removed.

        Returns:
            A dictionary containing the evaluation loss and the potential metrics computed from the predictions.
        """
        eval_dataloader = self.get_eval_dataloader(eval_dataset)

        output = self.prediction_loop(eval_dataloader, description="Evaluation")

        self.log(output.metrics)

        if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report())

        return output.metrics

    def predict(self, test_dataset: Dataset) -> PredictionOutput:
        """
        Run prediction and returns predictions and potential metrics.

        Depending on the dataset and your use case, your test dataset may contain labels.
        In that case, this method will also return metrics, like in :obj:`evaluate()`.

        Args:
            test_dataset (:obj:`Dataset`):
                Dataset to run the predictions on. If it is an :obj:`datasets.Dataset`, columns not accepted by the
                ``model.forward()`` method are automatically removed.

        Returns:
            `NamedTuple`:
            predictions (:obj:`np.ndarray`):
                The predictions on :obj:`test_dataset`.
            label_ids (:obj:`np.ndarray`, `optional`):
                The labels (if the dataset contained some).
            metrics (:obj:`Dict[str, float]`, `optional`):
                The potential dictionary of metrics (if the dataset contained labels).
        """
        test_dataloader = self.get_test_dataloader(test_dataset)

        return self.prediction_loop(test_dataloader, description="Prediction")

    def prediction_loop(
        self, dataloader: DataLoader, description: str, prediction_loss_only: Optional[bool] = None
    ) -> PredictionOutput:
        """
        Prediction/evaluation loop, shared by :obj:`Trainer.evaluate()` and :obj:`Trainer.predict()`.

        Works both with or without labels.
        """
        if hasattr(self, "_prediction_loop"):
            warnings.warn(
                "The `_prediction_loop` method is deprecated and won't be called in a future version, define `prediction_loop` in your subclass.",
                FutureWarning,
            )
            return self._prediction_loop(dataloader, description, prediction_loss_only=prediction_loss_only)

        prediction_loss_only = (
            prediction_loss_only if prediction_loss_only is not None else self.args.prediction_loss_only
        )

        model = self.model
        # multi-gpu eval
        if self.args.n_gpu > 1:
            model = torch.nn.DataParallel(model)
        else:
            model = self.model
        # Note: in torch.distributed mode, there's no point in wrapping the model
        # inside a DistributedDataParallel as we'll be under `no_grad` anyways.

        batch_size = dataloader.batch_size
        logger.info("***** Running %s *****", description)
        logger.info("  Num examples = %d", self.num_examples(dataloader))
        logger.info("  Batch size = %d", batch_size)
        eval_losses: List[float] = []
        preds: torch.Tensor = None
        label_ids: torch.Tensor = None
        model.eval()

        if is_torch_tpu_available():
            dataloader = pl.ParallelLoader(dataloader, [self.args.device]).per_device_loader(self.args.device)

        if self.args.past_index >= 0:
            self._past = None

        disable_tqdm = not self.is_local_process_zero() or self.args.disable_tqdm
        for inputs in tqdm(dataloader, desc=description, disable=disable_tqdm):
            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only)
            batch_size = inputs[list(inputs.keys())[0]].shape[0]
            if loss is not None:
                eval_losses.extend([loss] * batch_size)
            if logits is not None:
                preds = logits if preds is None else nested_concat(preds, logits, dim=0)
            if labels is not None:
                label_ids = labels if label_ids is None else nested_concat(label_ids, labels, dim=0)

        if self.args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        if self.args.local_rank != -1:
            # In distributed mode, concatenate all results from all nodes:
            if preds is not None:
                preds = distributed_concat(preds, num_total_examples=self.num_examples(dataloader))
            if label_ids is not None:
                label_ids = distributed_concat(label_ids, num_total_examples=self.num_examples(dataloader))
        elif is_torch_tpu_available():
            # tpu-comment: Get all predictions and labels from all worker shards of eval dataset
            if preds is not None:
                preds = nested_xla_mesh_reduce(preds, "eval_preds")
            if label_ids is not None:
                label_ids = nested_xla_mesh_reduce(label_ids, "eval_label_ids")
            if eval_losses is not None:
                eval_losses = xm.mesh_reduce("eval_losses", torch.tensor(eval_losses), torch.cat).tolist()

        # Finally, turn the aggregated tensors into numpy arrays.
        if preds is not None:
            preds = nested_numpify(preds)
        if label_ids is not None:
            label_ids = nested_numpify(label_ids)

        if self.compute_metrics is not None and preds is not None and label_ids is not None:
            metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
        else:
            metrics = {}
        if len(eval_losses) > 0:
            if self.args.local_rank != -1:
                metrics["eval_loss"] = (
                    distributed_broadcast_scalars(eval_losses, num_total_examples=self.num_examples(dataloader))
                    .mean()
                    .item()
                )
            else:
                metrics["eval_loss"] = np.mean(eval_losses)

        # Prefix all keys with eval_
        for key in list(metrics.keys()):
            if not key.startswith("eval_"):
                metrics[f"eval_{key}"] = metrics.pop(key)

        return PredictionOutput(predictions=preds, label_ids=label_ids, metrics=metrics)

    def prediction_step(
        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool
    ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on :obj:`model` using obj:`inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (:obj:`nn.Module`):
                The model to evaluate.
            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument :obj:`labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (:obj:`bool`):
                Whether or not to return the loss only.

        Return:
            Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
            A tuple with the loss, logits and labels (each being optional).
        """
        has_labels = all(inputs.get(k) is not None for k in self.label_names)
        inputs = self._prepare_inputs(inputs)

        with torch.no_grad():
            outputs = model(**inputs)
            if has_labels:
                # The .mean() is to reduce in case of distributed training
                loss = outputs[0].mean().item()
                logits = outputs[1:]
            else:
                loss = None
                # Slicing so we get a tuple even if `outputs` is a `ModelOutput`.
                logits = outputs[:]
            if self.args.past_index >= 0:
                self._past = outputs[self.args.past_index if has_labels else self.args.past_index - 1]
                # Remove the past from the logits.
                logits = logits[: self.args.past_index - 1] + logits[self.args.past_index :]

        if prediction_loss_only:
            return (loss, None, None)

        logits = nested_detach(logits)
        if len(logits) == 1:
            logits = logits[0]

        if has_labels:
            labels = nested_detach(tuple(inputs.get(name) for name in self.label_names))
            if len(labels) == 1:
                labels = labels[0]
        else:
            labels = None

        return (loss, logits, labels)

    def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
        """
        For models that inherit from :class:`~transformers.PretrainedModel`, uses
        that method to compute the number of floating point operations for every backward + forward pass. If using
        another model, either implement such a method in the model or subclass and override this method.

        Args:
            model (:obj:`nn.Module`):
                The model to evaluate.
            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

        Returns:
            :obj:`int`: The number of floating-point operations.
        """

        model = self._actual_model(self.model)

        if hasattr(model, "floating_point_ops"):
            return model.floating_point_ops(inputs)

        else:
            return 0

    @staticmethod
    def _actual_model(
        model: Union[torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, torch.nn.modules.Module]
    ) -> torch.nn.modules.Module:
        """

        Args:
            model: (:obj:`Union[torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, torch.nn.modules.Module]`):
                Model object used during training

        Returns:
            :obj:`torch.nn.modules.Module`: unwrapped module
        """
        if isinstance(model, torch.nn.DataParallel) or isinstance(model, torch.nn.parallel.DistributedDataParallel):
            model = model.module
        else:
            model = model
        return model