training_args.py

import dataclasses
import json
import logging
import os
from dataclasses import dataclass, field
from typing import Any, Dict, Optional, Tuple

from .file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required


if is_torch_available():
    import torch

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


logger = logging.getLogger(__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 arguments to be able to specify them 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.
        do_eval (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to run evaluation on the dev set or not.
        do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to run predictions on the test set or not.
        evaluate_during_training (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether to run evaluation during training at each logging step or not.
        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.
        learning_rate (:obj:`float`, `optional`, defaults to 5e-5):
            The initial learning rate for Adam.
        weight_decay (:obj:`float`, `optional`, defaults to 0):
            The weight decay to apply (if not zero).
        adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
            Epsilon for the Adam 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.
        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`.
        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 log directory. Will default to `runs/**CURRENT_DATETIME_HOSTNAME**`.
        logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Wheter to log and evalulate 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 for initialization.
        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>`__.
        local_rank (:obj:`int`, `optional`, defaults to -1):
            During distributed training, the rank of the process.
        tpu_num_cores (:obj:`int`, `optional`):
            When training on TPU, the mumber 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`, defaults to 1000):
            Number of update steps between two evaluations.
        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``.
    """

    output_dir: str = field(
        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=False, 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."})
    evaluate_during_training: bool = field(
        default=False, metadata={"help": "Run evaluation during training at each logging step."},
    )

    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."},
    )

    learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for Adam."})
    weight_decay: float = field(default=0.0, metadata={"help": "Weight decay if we apply some."})
    adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for Adam 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."},
    )
    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 and eval 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 for initialization"})

    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"
            )
        },
    )
    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=1000, metadata={"help": "Run an evaluation every X steps."})

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

    @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
        return per_device_batch_size * max(1, self.n_gpu)

    @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
        return per_device_batch_size * max(1, self.n_gpu)

    @cached_property
    @torch_required
    def _setup_devices(self) -> Tuple["torch.device", int]:
        logger.info("PyTorch: setting up devices")
        if self.no_cuda:
            device = torch.device("cpu")
            n_gpu = 0
        elif is_torch_tpu_available():
            device = xm.xla_device()
            n_gpu = 0
        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")
            n_gpu = torch.cuda.device_count()
        else:
            # Here, we'll use torch.distributed.
            # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
            torch.distributed.init_process_group(backend="nccl")
            device = torch.device("cuda", self.local_rank)
            n_gpu = 1

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

        return device, n_gpu

    @property
    @torch_required
    def device(self) -> "torch.device":
        """
        The device used by this process.
        """
        return self._setup_devices[0]

    @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.
        """
        return self._setup_devices[1]

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

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