training_args.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import asdict, dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional

from .file_utils import (
    cached_property,
    is_sagemaker_distributed_available,
    is_torch_available,
    is_torch_tpu_available,
    torch_required,
)
from .trainer_utils import EvaluationStrategy, SchedulerType
from .utils import logging


if is_torch_available():
    import torch

if is_torch_tpu_available():
    import torch_xla.core.xla_model as xm


logger = logging.get_logger(__name__)


def default_logdir() -> str:
    """
    Same default as PyTorch
    """
    import socket
    from datetime import datetime

    current_time = datetime.now().strftime("%b%d_%H-%M-%S")
    return os.path.join("runs", current_time + "_" + socket.gethostname())


@dataclass
class TrainingArguments:
    """
    TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
    itself**.

    Using :class:`~transformers.HfArgumentParser` we can turn this class into `argparse
    <https://docs.python.org/3/library/argparse.html#module-argparse>`__ arguments that can be specified on the command
    line.




    Parameters:
        output_dir (:obj:`str`):
            The output directory where the model predictions and checkpoints will be written.
        overwrite_output_dir (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If :obj:`True`, overwrite the content of the output directory. Use this to continue training if
            :obj:`output_dir` points to a checkpoint directory.
        do_train (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to run training or not. This argument is not directly used by :class:`~transformers.Trainer`, it's
            intended to be used by your training/evaluation scripts instead. See the `example scripts
            <https://github.com/huggingface/transformers/tree/master/examples>`__ for more details.
        do_eval (:obj:`bool`, `optional`):
            Whether to run evaluation on the validation set or not. Will be set to :obj:`True` if
            :obj:`evaluation_strategy` is different from :obj:`"no"`. This argument is not directly used by
            :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
            the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
            details.
        do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to run predictions on the test set or not. This argument is not directly used by
            :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
            the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
            details.
        evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`):
            The evaluation strategy to adopt during training. Possible values are:

                * :obj:`"no"`: No evaluation is done during training.
                * :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`.
                * :obj:`"epoch"`: Evaluation is done at the end of each epoch.

        prediction_loss_only (:obj:`bool`, `optional`, defaults to `False`):
            When performing evaluation and generating predictions, only returns the loss.
        per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8):
            The batch size per GPU/TPU core/CPU for training.
        per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8):
            The batch size per GPU/TPU core/CPU for evaluation.
        gradient_accumulation_steps (:obj:`int`, `optional`, defaults to 1):
            Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

            .. warning::

                When using gradient accumulation, one step is counted as one step with backward pass. Therefore,
                logging, evaluation, save will be conducted every ``gradient_accumulation_steps * xxx_step`` training
                examples.
        eval_accumulation_steps (:obj:`int`, `optional`):
            Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
            left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
            requires more memory).
        learning_rate (:obj:`float`, `optional`, defaults to 5e-5):
            The initial learning rate for :class:`~transformers.AdamW` optimizer.
        weight_decay (:obj:`float`, `optional`, defaults to 0):
            The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in
            :class:`~transformers.AdamW` optimizer.
        adam_beta1 (:obj:`float`, `optional`, defaults to 0.9):
            The beta1 hyperparameter for the :class:`~transformers.AdamW` optimizer.
        adam_beta2 (:obj:`float`, `optional`, defaults to 0.999):
            The beta2 hyperparameter for the :class:`~transformers.AdamW` optimizer.
        adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
            The epsilon hyperparameter for the :class:`~transformers.AdamW` optimizer.
        max_grad_norm (:obj:`float`, `optional`, defaults to 1.0):
            Maximum gradient norm (for gradient clipping).
        num_train_epochs(:obj:`float`, `optional`, defaults to 3.0):
            Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
            the last epoch before stopping training).
        max_steps (:obj:`int`, `optional`, defaults to -1):
            If set to a positive number, the total number of training steps to perform. Overrides
            :obj:`num_train_epochs`.
        lr_scheduler_type (:obj:`str` or :class:`~transformers.SchedulerType`, `optional`, defaults to :obj:`"linear"`):
            The scheduler type to use. See the documentation of :class:`~transformers.SchedulerType` for all possible
            values.
        warmup_steps (:obj:`int`, `optional`, defaults to 0):
            Number of steps used for a linear warmup from 0 to :obj:`learning_rate`.
        logging_dir (:obj:`str`, `optional`):
            `TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
            `runs/**CURRENT_DATETIME_HOSTNAME**`.
        logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to log and evaluate the first :obj:`global_step` or not.
        logging_steps (:obj:`int`, `optional`, defaults to 500):
            Number of update steps between two logs.
        save_steps (:obj:`int`, `optional`, defaults to 500):
            Number of updates steps before two checkpoint saves.
        save_total_limit (:obj:`int`, `optional`):
            If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
            :obj:`output_dir`.
        no_cuda (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to not use CUDA even when it is available or not.
        seed (:obj:`int`, `optional`, defaults to 42):
            Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
            :func:`~transformers.Trainer.model_init` function to instantiate the model if it has some randomly
            initialized parameters.
        fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.
        fp16_opt_level (:obj:`str`, `optional`, defaults to 'O1'):
            For :obj:`fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
            on the `Apex documentation <https://nvidia.github.io/apex/amp.html>`__.
        fp16_backend (:obj:`str`, `optional`, defaults to :obj:`"auto"`):
            The backend to use for mixed precision training. Must be one of :obj:`"auto"`, :obj:`"amp"` or
            :obj:`"apex"`. :obj:`"auto"` will use AMP or APEX depending on the PyTorch version detected, while the
            other choices will force the requested backend.
        local_rank (:obj:`int`, `optional`, defaults to -1):
            Rank of the process during distributed training.
        tpu_num_cores (:obj:`int`, `optional`):
            When training on TPU, the number of TPU cores (automatically passed by launcher script).
        debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
            When training on TPU, whether to print debug metrics or not.
        dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
            or not.
        eval_steps (:obj:`int`, `optional`):
            Number of update steps between two evaluations if :obj:`evaluation_strategy="steps"`. Will default to the
            same value as :obj:`logging_steps` if not set.
        dataloader_num_workers (:obj:`int`, `optional`, defaults to 0):
            Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
            main process.
        past_index (:obj:`int`, `optional`, defaults to -1):
            Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
            make use of the past hidden states for their predictions. If this argument is set to a positive int, the
            ``Trainer`` will use the corresponding output (usually index 2) as the past state and feed it to the model
            at the next training step under the keyword argument ``mems``.
        run_name (:obj:`str`, `optional`):
            A descriptor for the run. Typically used for `wandb <https://www.wandb.com/>`_ logging.
        disable_tqdm (:obj:`bool`, `optional`):
            Whether or not to disable the tqdm progress bars and table of metrics produced by
            :class:`~transformers.notebook.NotebookTrainingTracker` in Jupyter Notebooks. Will default to :obj:`True`
            if the logging level is set to warn or lower (default), :obj:`False` otherwise.
        remove_unused_columns (:obj:`bool`, `optional`, defaults to :obj:`True`):
            If using :obj:`datasets.Dataset` datasets, whether or not to automatically remove the columns unused by the
            model forward method.

            (Note that this behavior is not implemented for :class:`~transformers.TFTrainer` yet.)
        label_names (:obj:`List[str]`, `optional`):
            The list of keys in your dictionary of inputs that correspond to the labels.

            Will eventually default to :obj:`["labels"]` except if the model used is one of the
            :obj:`XxxForQuestionAnswering` in which case it will default to :obj:`["start_positions",
            "end_positions"]`.
        load_best_model_at_end (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to load the best model found during training at the end of training.

            .. note::

                When set to :obj:`True`, the parameters :obj:`save_steps` will be ignored and the model will be saved
                after each evaluation.
        metric_for_best_model (:obj:`str`, `optional`):
            Use in conjunction with :obj:`load_best_model_at_end` to specify the metric to use to compare two different
            models. Must be the name of a metric returned by the evaluation with or without the prefix :obj:`"eval_"`.
            Will default to :obj:`"loss"` if unspecified and :obj:`load_best_model_at_end=True` (to use the evaluation
            loss).

            If you set this value, :obj:`greater_is_better` will default to :obj:`True`. Don't forget to set it to
            :obj:`False` if your metric is better when lower.
        greater_is_better (:obj:`bool`, `optional`):
            Use in conjunction with :obj:`load_best_model_at_end` and :obj:`metric_for_best_model` to specify if better
            models should have a greater metric or not. Will default to:

            - :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or
              :obj:`"eval_loss"`.
            - :obj:`False` if :obj:`metric_for_best_model` is not set, or set to :obj:`"loss"` or :obj:`"eval_loss"`.
        ignore_skip_data (:obj:`bool`, `optional`, defaults to :obj:`False`):
            When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
            stage as in the previous training. If set to :obj:`True`, the training will begin faster (as that skipping
            step can take a long time) but will not yield the same results as the interrupted training would have.
        sharded_ddp (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Use Sharded DDP training from `FairScale <https://github.com/facebookresearch/fairscale>`__ (in distributed
            training only). This is an experimental feature.
        deepspeed (:obj:`str`, `optional`):
            Use `Deepspeed <https://github.com/microsoft/deepspeed>`__. This is an experimental feature and its API may
            evolve in the future. The value is the location of its json config file (usually ``ds_config.json``).
        label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0):
            The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
            labels are changed from 0s and 1s to :obj:`label_smoothing_factor/num_labels` and :obj:`1 -
            label_smoothing_factor + label_smoothing_factor/num_labels` respectively.
        adafactor (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to use the :class:`~transformers.Adafactor` optimizer instead of
            :class:`~transformers.AdamW`.
        group_by_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to group together samples of roughly the same legnth in the training dataset (to minimize
            padding applied and be more efficient). Only useful if applying dynamic padding.
        report_to (:obj:`str` or :obj:`List[str]`, `optional`, defaults to :obj:`"all"`):
            The list of integrations to report the results and logs to. Supported platforms are :obj:`"azure_ml"`,
            :obj:`"comet_ml"`, :obj:`"mlflow"`, :obj:`"tensorboard"` and :obj:`"wandb"`. Use :obj:`"all"` to report to
            all integrations installed, :obj:`"none"` for no integrations.
        ddp_find_unused_parameters (:obj:`bool`, `optional`):
            When using distributed training, the value of the flag :obj:`find_unused_parameters` passed to
            :obj:`DistributedDataParallel`. Will default to :obj:`False` if gradient checkpointing is used, :obj:`True`
            otherwise.
        dataloader_pin_memory (:obj:`bool`, `optional`, defaults to :obj:`True`)):
            Whether you want to pin memory in data loaders or not. Will default to :obj:`True`.
    """

    output_dir: Optional[str] = field(
        default=None,
        metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
    )
    overwrite_output_dir: bool = field(
        default=False,
        metadata={
            "help": (
                "Overwrite the content of the output directory."
                "Use this to continue training if output_dir points to a checkpoint directory."
            )
        },
    )

    do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
    do_eval: bool = field(default=None, metadata={"help": "Whether to run eval on the dev set."})
    do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
    evaluation_strategy: EvaluationStrategy = field(
        default="no",
        metadata={"help": "The evaluation strategy to use."},
    )
    prediction_loss_only: bool = field(
        default=False,
        metadata={"help": "When performing evaluation and predictions, only returns the loss."},
    )

    per_device_train_batch_size: int = field(
        default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
    )
    per_device_eval_batch_size: int = field(
        default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
    )

    per_gpu_train_batch_size: Optional[int] = field(
        default=None,
        metadata={
            "help": "Deprecated, the use of `--per_device_train_batch_size` is preferred. "
            "Batch size per GPU/TPU core/CPU for training."
        },
    )
    per_gpu_eval_batch_size: Optional[int] = field(
        default=None,
        metadata={
            "help": "Deprecated, the use of `--per_device_eval_batch_size` is preferred."
            "Batch size per GPU/TPU core/CPU for evaluation."
        },
    )

    gradient_accumulation_steps: int = field(
        default=1,
        metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."},
    )
    eval_accumulation_steps: Optional[int] = field(
        default=None,
        metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."},
    )

    learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
    weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
    adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
    adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
    adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
    max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."})

    num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
    max_steps: int = field(
        default=-1,
        metadata={"help": "If > 0: set total number of training steps to perform. Override num_train_epochs."},
    )
    lr_scheduler_type: SchedulerType = field(
        default="linear",
        metadata={"help": "The scheduler type to use."},
    )
    warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})

    logging_dir: Optional[str] = field(default_factory=default_logdir, metadata={"help": "Tensorboard log dir."})
    logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"})
    logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
    save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
    save_total_limit: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "Limit the total amount of checkpoints."
                "Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints"
            )
        },
    )
    no_cuda: bool = field(default=False, metadata={"help": "Do not use CUDA even when it is available"})
    seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})

    fp16: bool = field(
        default=False,
        metadata={"help": "Whether to use 16-bit (mixed) precision (through NVIDIA Apex) instead of 32-bit"},
    )
    fp16_opt_level: str = field(
        default="O1",
        metadata={
            "help": (
                "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                "See details at https://nvidia.github.io/apex/amp.html"
            )
        },
    )
    fp16_backend: str = field(
        default="auto",
        metadata={"help": "The backend to be used for mixed precision.", "choices": ["auto", "amp", "apex"]},
    )
    local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"})

    tpu_num_cores: Optional[int] = field(
        default=None, metadata={"help": "TPU: Number of TPU cores (automatically passed by launcher script)"}
    )
    tpu_metrics_debug: bool = field(
        default=False,
        metadata={"help": "Deprecated, the use of `--debug` is preferred. TPU: Whether to print debug metrics"},
    )
    debug: bool = field(default=False, metadata={"help": "Whether to print debug metrics on TPU"})

    dataloader_drop_last: bool = field(
        default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."}
    )
    eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
    dataloader_num_workers: int = field(
        default=0,
        metadata={
            "help": "Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process."
        },
    )

    past_index: int = field(
        default=-1,
        metadata={"help": "If >=0, uses the corresponding part of the output as the past state for next step."},
    )

    run_name: Optional[str] = field(
        default=None, metadata={"help": "An optional descriptor for the run. Notably used for wandb logging."}
    )
    disable_tqdm: Optional[bool] = field(
        default=None, metadata={"help": "Whether or not to disable the tqdm progress bars."}
    )

    remove_unused_columns: Optional[bool] = field(
        default=True, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."}
    )
    label_names: Optional[List[str]] = field(
        default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."}
    )

    load_best_model_at_end: Optional[bool] = field(
        default=False,
        metadata={"help": "Whether or not to load the best model found during training at the end of training."},
    )
    metric_for_best_model: Optional[str] = field(
        default=None, metadata={"help": "The metric to use to compare two different models."}
    )
    greater_is_better: Optional[bool] = field(
        default=None, metadata={"help": "Whether the `metric_for_best_model` should be maximized or not."}
    )
    ignore_data_skip: bool = field(
        default=False,
        metadata={
            "help": "When resuming training, whether or not to skip the first epochs and batches to get to the same training data."
        },
    )
    sharded_ddp: bool = field(
        default=False,
        metadata={"help": "Whether or not to use sharded DDP training (in distributed training only)."},
    )
    deepspeed: Optional[str] = field(
        default=None,
        metadata={"help": "Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json)"},
    )
    label_smoothing_factor: float = field(
        default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
    )
    adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
    group_by_length: bool = field(
        default=False,
        metadata={"help": "Whether or not to group samples of roughly the same length together when batching."},
    )
    report_to: Optional[List[str]] = field(
        default=None, metadata={"help": "The list of integrations to report the results and logs to."}
    )
    ddp_find_unused_parameters: Optional[bool] = field(
        default=None,
        metadata={
            "help": "When using distributed training, the value of the flag `find_unused_parameters` passed to "
            "`DistributedDataParallel`."
        },
    )
    dataloader_pin_memory: bool = field(
        default=True, metadata={"help": "Whether or not to pin memory for DataLoader."}
    )
    _n_gpu: int = field(init=False, repr=False, default=-1)

    def __post_init__(self):
        if self.output_dir is None and os.getenv("SM_OUTPUT_DATA_DIR") is None:
            raise ValueError(
                "`output_dir` is only optional if it can get inferred from the environment. Please set a value for "
                "`output_dir`."
            )
        elif os.getenv("SM_OUTPUT_DATA_DIR") is not None:
            if self.output_dir is not None:
                logger.warn(
                    "`output_dir` is overwritten by the env variable 'SM_OUTPUT_DATA_DIR' "
                    f"({os.getenv('SM_OUTPUT_DATA_DIR')})."
                )
            self.output_dir = os.getenv("SM_OUTPUT_DATA_DIR")
        if self.disable_tqdm is None:
            self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
        self.evaluation_strategy = EvaluationStrategy(self.evaluation_strategy)
        self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
        if self.do_eval is False and self.evaluation_strategy != EvaluationStrategy.NO:
            self.do_eval = True
        if self.eval_steps is None:
            self.eval_steps = self.logging_steps

        if self.load_best_model_at_end and self.metric_for_best_model is None:
            self.metric_for_best_model = "loss"
        if self.greater_is_better is None and self.metric_for_best_model is not None:
            self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"]
        if self.run_name is None:
            self.run_name = self.output_dir

        if is_torch_available() and self.device.type != "cuda" and self.fp16:
            raise ValueError("Mixed precision training with AMP or APEX (`--fp16`) can only be used on CUDA devices.")
        if self.report_to is None:
            logger.info(
                "The default value for the training argument `--report_to` will change in v5 (from all installed "
                "integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as "
                "now. You should start updating your code and make this info disappear :-)."
            )
            self.report_to = "all"
        if self.report_to == "all" or self.report_to == ["all"]:
            # Import at runtime to avoid a circular import.
            from .integrations import get_available_reporting_integrations

            self.report_to = get_available_reporting_integrations()
        elif self.report_to == "none" or self.report_to == ["none"]:
            self.report_to = []
        elif not isinstance(self.report_to, list):
            self.report_to = [self.report_to]

    def __repr__(self):
        # We override the default repr to remove deprecated arguments from the repr. This method should be removed once
        # those deprecated arguments are removed form TrainingArguments. (TODO: v5)
        self_as_dict = asdict(self)
        del self_as_dict["per_gpu_train_batch_size"]
        del self_as_dict["per_gpu_eval_batch_size"]
        attrs_as_str = [f"{k}={v}" for k, v in self_as_dict.items()]
        return f"{self.__class__.__name__}({', '.join(attrs_as_str)})"

    @property
    def train_batch_size(self) -> int:
        """
        The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training).
        """
        if self.per_gpu_train_batch_size:
            logger.warning(
                "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
                "version. Using `--per_device_train_batch_size` is preferred."
            )
        per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
        train_batch_size = per_device_batch_size * max(1, self.n_gpu)
        return train_batch_size

    @property
    def eval_batch_size(self) -> int:
        """
        The actual batch size for evaluation (may differ from :obj:`per_gpu_eval_batch_size` in distributed training).
        """
        if self.per_gpu_eval_batch_size:
            logger.warning(
                "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
                "version. Using `--per_device_eval_batch_size` is preferred."
            )
        per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
        eval_batch_size = per_device_batch_size * max(1, self.n_gpu)
        return eval_batch_size

    @cached_property
    @torch_required
    def _setup_devices(self) -> "torch.device":
        logger.info("PyTorch: setting up devices")
        if self.no_cuda:
            device = torch.device("cpu")
            self._n_gpu = 0
        elif is_torch_tpu_available():
            device = xm.xla_device()
            self._n_gpu = 0
        elif is_sagemaker_distributed_available():
            import smdistributed.dataparallel.torch.distributed as dist

            dist.init_process_group()
            self.local_rank = dist.get_local_rank()
            device = torch.device("cuda", self.local_rank)
            self._n_gpu = 1
        elif self.deepspeed:
            # deepspeed performs its own DDP internally, and requires the program to be started with:
            # deepspeed  ./program.py
            # rather than:
            # python -m torch.distributed.launch --nproc_per_node=2 ./program.py
            from .integrations import is_deepspeed_available

            if not is_deepspeed_available():
                raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.")
            import deepspeed

            deepspeed.init_distributed()

            # workaround for setups like notebooks where the launcher can't be used,
            # but deepspeed requires a dist env.
            # env LOCAL_RANK could be set manually by the user, or via init_distributed if mpi4py is installed
            self.local_rank = int(os.environ.get("LOCAL_RANK", "-1"))

            device = torch.device("cuda", self.local_rank)
            self._n_gpu = 1
        elif self.local_rank == -1:
            # if n_gpu is > 1 we'll use nn.DataParallel.
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
            # trigger an error that a device index is missing. Index 0 takes into account the
            # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
            # will use the first GPU in that env, i.e. GPU#1
            device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
            # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
            # the default value.
            self._n_gpu = torch.cuda.device_count()
        else:
            # Here, we'll use torch.distributed.
            # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
            torch.distributed.init_process_group(backend="nccl")
            device = torch.device("cuda", self.local_rank)
            self._n_gpu = 1

        if device.type == "cuda":
            torch.cuda.set_device(device)

        return device

    @property
    @torch_required
    def device(self) -> "torch.device":
        """
        The device used by this process.
        """
        return self._setup_devices

    @property
    @torch_required
    def n_gpu(self):
        """
        The number of GPUs used by this process.

        Note:
            This will only be greater than one when you have multiple GPUs available but are not using distributed
            training. For distributed training, it will always be 1.
        """
        # Make sure `self._n_gpu` is properly setup.
        _ = self._setup_devices
        return self._n_gpu

    @property
    @torch_required
    def parallel_mode(self):
        """
        The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:

        - :obj:`ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
        - :obj:`ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses :obj:`torch.nn.DataParallel`).
        - :obj:`ParallelMode.DISTRIBUTED`: several GPUs, each ahving its own process (uses
          :obj:`torch.nn.DistributedDataParallel`).
        - :obj:`ParallelMode.TPU`: several TPU cores.
        """
        if is_torch_tpu_available():
            return ParallelMode.TPU
        elif is_sagemaker_distributed_available():
            return ParallelMode.SAGEMAKER_DISTRIBUTED
        elif self.local_rank != -1:
            return ParallelMode.DISTRIBUTED
        elif self.n_gpu > 1:
            return ParallelMode.NOT_DISTRIBUTED
        else:
            return ParallelMode.NOT_PARALLEL

    @property
    def place_model_on_device(self):
        """
        Can be subclassed and overridden for some specific integrations.
        """
        return True

    def to_dict(self):
        """
        Serializes this instance while replace `Enum` by their values (for JSON serialization support).
        """
        d = asdict(self)
        for k, v in d.items():
            if isinstance(v, Enum):
                d[k] = v.value
        return d

    def to_json_string(self):
        """
        Serializes this instance to a JSON string.
        """
        return json.dumps(self.to_dict(), indent=2)

    def to_sanitized_dict(self) -> Dict[str, Any]:
        """
        Sanitized serialization to use with TensorBoard’s hparams
        """
        d = self.to_dict()
        d = {**d, **{"train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size}}

        valid_types = [bool, int, float, str]
        if is_torch_available():
            valid_types.append(torch.Tensor)

        return {k: v if type(v) in valid_types else str(v) for k, v in d.items()}


class ParallelMode(Enum):
    NOT_PARALLEL = "not_parallel"
    NOT_DISTRIBUTED = "not_distributed"
    DISTRIBUTED = "distributed"
    SAGEMAKER_DISTRIBUTED = "sm_distributed"
    TPU = "tpu"