lightning.py

import collections
import inspect
import os
import warnings
from abc import ABC, abstractmethod
from argparse import Namespace
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence

import torch
import torch.distributed as torch_distrib
from torch import Tensor
from torch.nn.parallel import DistributedDataParallel
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader

from pytorch_lightning import _logger as log
from pytorch_lightning.core.grads import GradInformation
from pytorch_lightning.core.hooks import ModelHooks
from pytorch_lightning.core.memory import ModelSummary
from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv, load_hparams_from_yaml
from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities import rank_zero_warn

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

CHECKPOINT_KEY_MODULE_ARGS = 'module_arguments'


class LightningModule(ABC, DeviceDtypeModuleMixin, GradInformation, ModelIO, ModelHooks):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.exp_save_path = None

        #: The current epoch
        self.current_epoch = 0

        #: Total training batches seen across all epochs
        self.global_step = 0

        self.loaded_optimizer_states_dict = {}

        #: Pointer to the trainer object
        self.trainer = None

        #: Pointer to the logger object
        self.logger = None
        self.example_input_array = None

        #: True if using dp
        self.use_dp = False

        #: True if using ddp
        self.use_ddp = False

        #: True if using ddp2
        self.use_ddp2 = False

        # True if on tpu
        self.use_tpu = False

        #: True if using amp
        self.use_amp = False

        #: Current dtype
        self._dtype = torch.float

        #: device reference
        self._device = torch.device('cpu')

        # register all params passed into the child module in __init__
        self._auto_collect_arguments()

    @property
    def on_gpu(self):
        """
        True if your model is currently running on GPUs.
        Useful to set flags around the LightningModule for different CPU vs GPU behavior.
        """
        return self.device.type == 'cuda'

    def print(self, *args, **kwargs) -> None:
        r"""
        Prints only from process 0. Use this in any distributed mode to log only once.

        Args:
            *args: The thing to print. Will be passed to Python's built-in print function.
            **kwargs: Will be passed to Python's built-in print function.

        Example:

            .. code-block:: python

                def forward(self, x):
                    self.print(x, 'in forward')

        """
        if self.trainer.proc_rank == 0:
            print(*args, **kwargs)

    @abstractmethod
    def forward(self, *args, **kwargs):
        r"""
        Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define
        the operations you want to use for prediction (i.e.: on a server or as a feature extractor).

        Normally you'd call ``self()`` from your :meth:`training_step` method.
        This makes it easy to write a complex system for training with the outputs
        you'd want in a prediction setting.

        Args:
            *args: Whatever you decide to pass into the forward method.
            **kwargs: Keyword arguments are also possible.

        Return:
            Predicted output

        Examples:
            .. code-block:: python

                # example if we were using this model as a feature extractor
                def forward(self, x):
                    feature_maps = self.convnet(x)
                    return feature_maps

                def training_step(self, batch, batch_idx):
                    x, y = batch
                    feature_maps = self(x)
                    logits = self.classifier(feature_maps)

                    # ...
                    return loss

                # splitting it this way allows model to be used a feature extractor
                model = MyModelAbove()

                inputs = server.get_request()
                results = model(inputs)
                server.write_results(results)

                # -------------
                # This is in stark contrast to torch.nn.Module where normally you would have this:
                def forward(self, batch):
                    x, y = batch
                    feature_maps = self.convnet(x)
                    logits = self.classifier(feature_maps)
                    return logits

        """

    def training_step(self, *args, **kwargs) -> Union[
        int, Dict[str, Union[Tensor, Dict[str, Tensor]]]
    ]:
        r"""
        Here you compute and return the training loss and some additional metrics for e.g.
        the progress bar or logger.

        Args:
            batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
                The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
            batch_idx (int): Integer displaying index of this batch
            optimizer_idx (int): When using multiple optimizers, this argument will also be present.
            hiddens(:class:`~torch.Tensor`): Passed in if
                :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.

        Return:
            Dict with loss key and optional log or progress bar keys.
            When implementing :meth:`training_step`, return whatever you need in that step:

            - loss -> tensor scalar **REQUIRED**
            - progress_bar -> Dict for progress bar display. Must have only tensors
            - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)

        In this step you'd normally do the forward pass and calculate the loss for a batch.
        You can also do fancier things like multiple forward passes or something model specific.

        Examples:
            .. code-block:: python

                def training_step(self, batch, batch_idx):
                    x, y, z = batch

                    # implement your own
                    out = self(x)
                    loss = self.loss(out, x)

                    logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)

                    # if using TestTubeLogger or TensorBoardLogger you can nest scalars
                    logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)

                    output = {
                        'loss': loss, # required
                        'progress_bar': {'training_loss': loss}, # optional (MUST ALL BE TENSORS)
                        'log': logger_logs
                    }

                    # return a dict
                    return output

            If you define multiple optimizers, this step will be called with an additional
            ``optimizer_idx`` parameter.

            .. code-block:: python

                # Multiple optimizers (e.g.: GANs)
                def training_step(self, batch, batch_idx, optimizer_idx):
                    if optimizer_idx == 0:
                        # do training_step with encoder
                    if optimizer_idx == 1:
                        # do training_step with decoder


            If you add truncated back propagation through time you will also get an additional
            argument with the hidden states of the previous step.

            .. code-block:: python

                # Truncated back-propagation through time
                def training_step(self, batch, batch_idx, hiddens):
                    # hiddens are the hidden states from the previous truncated backprop step
                    ...
                    out, hiddens = self.lstm(data, hiddens)
                    ...

                    return {
                        "loss": ...,
                        "hiddens": hiddens  # remember to detach() this
                    }

        Notes:
            The loss value shown in the progress bar is smoothed (averaged) over the last values,
            so it differs from the actual loss returned in train/validation step.
        """
        rank_zero_warn('`training_step` must be implemented to be used with the Lightning Trainer')

    def training_end(self, *args, **kwargs):
        """
        Warnings:
            Deprecated in v0.7.0. Use  :meth:`training_step_end` instead.
        """

    def training_epoch_end(
            self,
            outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]
    ) -> Dict[str, Dict[str, Tensor]]:
        """Called at the end of the training epoch with the outputs of all training steps.

        .. code-block:: python

            # the pseudocode for these calls
            train_outs = []
            for train_batch in train_data:
                out = training_step(train_batch)
                train_outs.append(out)
            training_epoch_end(train_outs)

        Args:
            outputs: List of outputs you defined in :meth:`training_step`, or if there are
                multiple dataloaders, a list containing a list of outputs for each dataloader.

        Return:
            Dict or OrderedDict.
            May contain the following optional keys:

            - log (metrics to be added to the logger; only tensors)
            - progress_bar (dict for progress bar display)
            - any metric used in a callback (e.g. early stopping).

        Note:
            If this method is not overridden, this won't be called.

        - The outputs here are strictly for logging or progress bar.
        - If you don't need to display anything, don't return anything.
        - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.

        Examples:
            With a single dataloader:

            .. code-block:: python

                def training_epoch_end(self, outputs):
                    train_acc_mean = 0
                    for output in outputs:
                        train_acc_mean += output['train_acc']

                    train_acc_mean /= len(outputs)

                    # log training accuracy at the end of an epoch
                    results = {
                        'log': {'train_acc': train_acc_mean.item()},
                        'progress_bar': {'train_acc': train_acc_mean},
                    }
                    return results

            With multiple dataloaders, ``outputs`` will be a list of lists. The outer list contains
            one entry per dataloader, while the inner list contains the individual outputs of
            each training step for that dataloader.

            .. code-block:: python

                def training_epoch_end(self, outputs):
                    train_acc_mean = 0
                    i = 0
                    for dataloader_outputs in outputs:
                        for output in dataloader_outputs:
                            train_acc_mean += output['train_acc']
                            i += 1

                    train_acc_mean /= i

                    # log training accuracy at the end of an epoch
                    results = {
                        'log': {'train_acc': train_acc_mean.item(), 'step': self.current_epoch}
                        'progress_bar': {'train_acc': train_acc_mean},
                    }
                    return results
        """

    def training_step_end(self, *args, **kwargs) -> Dict[
        str, Union[Tensor, Dict[str, Tensor]]
    ]:
        """
        Use this when training with dp or ddp2 because :meth:`training_step`
        will operate on only part of the batch. However, this is still optional
        and only needed for things like softmax or NCE loss.

        Note:
            If you later switch to ddp or some other mode, this will still be called
            so that you don't have to change your code

        .. code-block:: python

            # pseudocode
            sub_batches = split_batches_for_dp(batch)
            batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]
            training_step_end(batch_parts_outputs)

        Args:
            batch_parts_outputs: What you return in `training_step` for each batch part.

        Return:
            Dict with loss key and optional log or progress bar keys.

            - loss -> tensor scalar **REQUIRED**
            - progress_bar -> Dict for progress bar display. Must have only tensors
            - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)

        Examples:
            .. code-block:: python

                # WITHOUT training_step_end
                # if used in DP or DDP2, this batch is 1/num_gpus large
                def training_step(self, batch, batch_idx):
                    # batch is 1/num_gpus big
                    x, y = batch

                    out = self(x)
                    loss = self.softmax(out)
                    loss = nce_loss(loss)
                    return {'loss': loss}

                # --------------
                # with training_step_end to do softmax over the full batch
                def training_step(self, batch, batch_idx):
                    # batch is 1/num_gpus big
                    x, y = batch

                    out = self(x)
                    return {'out': out}

                def training_step_end(self, outputs):
                    # this out is now the full size of the batch
                    out = outputs['out']

                    # this softmax now uses the full batch size
                    loss = nce_loss(loss)
                    return {'loss': loss}

        See Also:
            See the :ref:`multi-gpu-training` guide for more details.
        """

    def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:
        r"""
        Operates on a single batch of data from the validation set.
        In this step you'd might generate examples or calculate anything of interest like accuracy.

        .. code-block:: python

            # the pseudocode for these calls
            val_outs = []
            for val_batch in val_data:
                out = validation_step(train_batch)
                val_outs.append(out)
                validation_epoch_end(val_outs)

        Args:
            batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
                The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
            batch_idx (int): The index of this batch
            dataloader_idx (int): The index of the dataloader that produced this batch
                (only if multiple val datasets used)

        Return:
            Dict or OrderedDict - passed to :meth:`validation_epoch_end`.
            If you defined :meth:`validation_step_end` it will go to that first.

        .. code-block:: python

            # pseudocode of order
            out = validation_step()
            if defined('validation_step_end'):
                out = validation_step_end(out)
            out = validation_epoch_end(out)


        .. code-block:: python

            # if you have one val dataloader:
            def validation_step(self, batch, batch_idx)

            # if you have multiple val dataloaders:
            def validation_step(self, batch, batch_idx, dataloader_idx)

        Examples:
            .. code-block:: python

                # CASE 1: A single validation dataset
                def validation_step(self, batch, batch_idx):
                    x, y = batch

                    # implement your own
                    out = self(x)
                    loss = self.loss(out, y)

                    # log 6 example images
                    # or generated text... or whatever
                    sample_imgs = x[:6]
                    grid = torchvision.utils.make_grid(sample_imgs)
                    self.logger.experiment.add_image('example_images', grid, 0)

                    # calculate acc
                    labels_hat = torch.argmax(out, dim=1)
                    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

                    # all optional...
                    # return whatever you need for the collation function validation_epoch_end
                    output = OrderedDict({
                        'val_loss': loss_val,
                        'val_acc': torch.tensor(val_acc), # everything must be a tensor
                    })

                    # return an optional dict
                    return output

            If you pass in multiple val datasets, validation_step will have an additional argument.

            .. code-block:: python

                # CASE 2: multiple validation datasets
                def validation_step(self, batch, batch_idx, dataset_idx):
                    # dataset_idx tells you which dataset this is.

        Note:
            If you don't need to validate you don't need to implement this method.

        Note:
            When the :meth:`validation_step` is called, the model has been put in eval mode
            and PyTorch gradients have been disabled. At the end of validation,
            the model goes back to training mode and gradients are enabled.
        """

    def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:
        """
        Use this when validating with dp or ddp2 because :meth:`validation_step`
        will operate on only part of the batch. However, this is still optional
        and only needed for things like softmax or NCE loss.

        Note:
            If you later switch to ddp or some other mode, this will still be called
            so that you don't have to change your code.

        .. code-block:: python

            # pseudocode
            sub_batches = split_batches_for_dp(batch)
            batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]
            validation_step_end(batch_parts_outputs)

        Args:
            batch_parts_outputs: What you return in :meth:`validation_step`
                for each batch part.

        Return:
           Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.

        Examples:
            .. code-block:: python

                # WITHOUT validation_step_end
                # if used in DP or DDP2, this batch is 1/num_gpus large
                def validation_step(self, batch, batch_idx):
                    # batch is 1/num_gpus big
                    x, y = batch

                    out = self(x)
                    loss = self.softmax(out)
                    loss = nce_loss(loss)
                    return {'loss': loss}

                # --------------
                # with validation_step_end to do softmax over the full batch
                def validation_step(self, batch, batch_idx):
                    # batch is 1/num_gpus big
                    x, y = batch

                    out = self(x)
                    return {'out': out}

                def validation_epoch_end(self, outputs):
                    # this out is now the full size of the batch
                    out = outputs['out']

                    # this softmax now uses the full batch size
                    loss = nce_loss(loss)
                    return {'loss': loss}

        See Also:
            See the :ref:`multi-gpu-training` guide for more details.
        """

    def validation_end(self, outputs):
        """
        Warnings:
            Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.
            Will be removed in 1.0.0.
        """

    def validation_epoch_end(
            self,
            outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]
    ) -> Dict[str, Dict[str, Tensor]]:
        """
        Called at the end of the validation epoch with the outputs of all validation steps.

        .. code-block:: python

            # the pseudocode for these calls
            val_outs = []
            for val_batch in val_data:
                out = validation_step(val_batch)
                val_outs.append(out)
            validation_epoch_end(val_outs)

        Args:
            outputs: List of outputs you defined in :meth:`validation_step`, or if there
                are multiple dataloaders, a list containing a list of outputs for each dataloader.

        Return:
            Dict or OrderedDict.
            May have the following optional keys:

            - progress_bar (dict for progress bar display; only tensors)
            - log (dict of metrics to add to logger; only tensors).

        Note:
            If you didn't define a :meth:`validation_step`, this won't be called.

        - The outputs here are strictly for logging or progress bar.
        - If you don't need to display anything, don't return anything.
        - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.

        Examples:
            With a single dataloader:

            .. code-block:: python

                def validation_epoch_end(self, outputs):
                    val_acc_mean = 0
                    for output in outputs:
                        val_acc_mean += output['val_acc']

                    val_acc_mean /= len(outputs)
                    tqdm_dict = {'val_acc': val_acc_mean.item()}

                    # show val_acc in progress bar but only log val_loss
                    results = {
                        'progress_bar': tqdm_dict,
                        'log': {'val_acc': val_acc_mean.item()}
                    }
                    return results

            With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
            one entry per dataloader, while the inner list contains the individual outputs of
            each validation step for that dataloader.

            .. code-block:: python

                def validation_epoch_end(self, outputs):
                    val_acc_mean = 0
                    i = 0
                    for dataloader_outputs in outputs:
                        for output in dataloader_outputs:
                            val_acc_mean += output['val_acc']
                            i += 1

                    val_acc_mean /= i
                    tqdm_dict = {'val_acc': val_acc_mean.item()}

                    # show val_loss and val_acc in progress bar but only log val_loss
                    results = {
                        'progress_bar': tqdm_dict,
                        'log': {'val_acc': val_acc_mean.item(), 'step': self.current_epoch}
                    }
                    return results
        """

    def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:
        r"""
        Operates on a single batch of data from the test set.
        In this step you'd normally generate examples or calculate anything of interest
        such as accuracy.

        .. code-block:: python

            # the pseudocode for these calls
            test_outs = []
            for test_batch in test_data:
                out = test_step(test_batch)
                test_outs.append(out)
            test_epoch_end(test_outs)

        Args:
            batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
                The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
            batch_idx (int): The index of this batch.
            dataloader_idx (int): The index of the dataloader that produced this batch
                (only if multiple test datasets used).

        Return:
            Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.
            If you defined :meth:`test_step_end` it will go to that first.

        .. code-block:: python

            # if you have one test dataloader:
            def test_step(self, batch, batch_idx)

            # if you have multiple test dataloaders:
            def test_step(self, batch, batch_idx, dataloader_idx)

        Examples:
            .. code-block:: python

                # CASE 1: A single test dataset
                def test_step(self, batch, batch_idx):
                    x, y = batch

                    # implement your own
                    out = self(x)
                    loss = self.loss(out, y)

                    # log 6 example images
                    # or generated text... or whatever
                    sample_imgs = x[:6]
                    grid = torchvision.utils.make_grid(sample_imgs)
                    self.logger.experiment.add_image('example_images', grid, 0)

                    # calculate acc
                    labels_hat = torch.argmax(out, dim=1)
                    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

                    # all optional...
                    # return whatever you need for the collation function test_epoch_end
                    output = OrderedDict({
                        'val_loss': loss_val,
                        'val_acc': torch.tensor(val_acc), # everything must be a tensor
                    })

                    # return an optional dict
                    return output

            If you pass in multiple validation datasets, :meth:`test_step` will have an additional
            argument.

            .. code-block:: python

                # CASE 2: multiple test datasets
                def test_step(self, batch, batch_idx, dataset_idx):
                    # dataset_idx tells you which dataset this is.

        Note:
            If you don't need to validate you don't need to implement this method.

        Note:
            When the :meth:`test_step` is called, the model has been put in eval mode and
            PyTorch gradients have been disabled. At the end of the test epoch, the model goes back
            to training mode and gradients are enabled.
        """

    def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:
        """
        Use this when testing with dp or ddp2 because :meth:`test_step` will operate
        on only part of the batch. However, this is still optional
        and only needed for things like softmax or NCE loss.

        Note:
            If you later switch to ddp or some other mode, this will still be called
            so that you don't have to change your code.

        .. code-block:: python

            # pseudocode
            sub_batches = split_batches_for_dp(batch)
            batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]
            test_step_end(batch_parts_outputs)

        Args:
            batch_parts_outputs: What you return in :meth:`test_step` for each batch part.

        Return:
             Dict or OrderedDict - passed to the :meth:`test_epoch_end`.

        Examples:
            .. code-block:: python

                # WITHOUT test_step_end
                # if used in DP or DDP2, this batch is 1/num_gpus large
                def test_step(self, batch, batch_idx):
                    # batch is 1/num_gpus big
                    x, y = batch

                    out = self(x)
                    loss = self.softmax(out)
                    loss = nce_loss(loss)
                    return {'loss': loss}

                # --------------
                # with test_step_end to do softmax over the full batch
                def test_step(self, batch, batch_idx):
                    # batch is 1/num_gpus big
                    x, y = batch

                    out = self(x)
                    return {'out': out}

                def test_step_end(self, outputs):
                    # this out is now the full size of the batch
                    out = outputs['out']

                    # this softmax now uses the full batch size
                    loss = nce_loss(loss)
                    return {'loss': loss}

        See Also:
            See the :ref:`multi-gpu-training` guide for more details.
        """

    def test_end(self, outputs):
        """
        Warnings:
             Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.
             Will be removed in 1.0.0.
        """

    def test_epoch_end(
            self,
            outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]
    ) -> Dict[str, Dict[str, Tensor]]:
        """
        Called at the end of a test epoch with the output of all test steps.

        .. code-block:: python

            # the pseudocode for these calls
            test_outs = []
            for test_batch in test_data:
                out = test_step(test_batch)
                test_outs.append(out)
            test_epoch_end(test_outs)

        Args:
            outputs: List of outputs you defined in :meth:`test_step_end`, or if there
                are multiple dataloaders, a list containing a list of outputs for each dataloader

        Return:
            Dict or OrderedDict: Dict has the following optional keys:

            - progress_bar -> Dict for progress bar display. Must have only tensors.
            - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).

        Note:
            If you didn't define a :meth:`test_step`, this won't be called.

        - The outputs here are strictly for logging or progress bar.
        - If you don't need to display anything, don't return anything.
        - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict

        Examples:
            With a single dataloader:

            .. code-block:: python

                def test_epoch_end(self, outputs):
                    test_acc_mean = 0
                    for output in outputs:
                        test_acc_mean += output['test_acc']

                    test_acc_mean /= len(outputs)
                    tqdm_dict = {'test_acc': test_acc_mean.item()}

                    # show test_loss and test_acc in progress bar but only log test_loss
                    results = {
                        'progress_bar': tqdm_dict,
                        'log': {'test_acc': test_acc_mean.item()}
                    }
                    return results

            With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
            one entry per dataloader, while the inner list contains the individual outputs of
            each test step for that dataloader.

            .. code-block:: python

                def test_epoch_end(self, outputs):
                    test_acc_mean = 0
                    i = 0
                    for dataloader_outputs in outputs:
                        for output in dataloader_outputs:
                            test_acc_mean += output['test_acc']
                            i += 1

                    test_acc_mean /= i
                    tqdm_dict = {'test_acc': test_acc_mean.item()}

                    # show test_loss and test_acc in progress bar but only log test_loss
                    results = {
                        'progress_bar': tqdm_dict,
                        'log': {'test_acc': test_acc_mean.item(), 'step': self.current_epoch}
                    }
                    return results
        """

    def configure_ddp(
            self,
            model: 'LightningModule',
            device_ids: List[int]
    ) -> DistributedDataParallel:
        r"""
        Override to init DDP in your own way or with your own wrapper.
        The only requirements are that:

        1. On a validation batch the call goes to ``model.validation_step``.
        2. On a training batch the call goes to ``model.training_step``.
        3. On a testing batch, the call goes to ``model.test_step``.+

        Args:
            model: the :class:`LightningModule` currently being optimized.
            device_ids: the list of GPU ids.

        Return:
            DDP wrapped model

        Examples:
            .. code-block:: python

                # default implementation used in Trainer
                def configure_ddp(self, model, device_ids):
                    # Lightning DDP simply routes to test_step, val_step, etc...
                    model = LightningDistributedDataParallel(
                        model,
                        device_ids=device_ids,
                        find_unused_parameters=True
                    )
                    return model

        """
        model = LightningDistributedDataParallel(
            model,
            device_ids=device_ids,
            find_unused_parameters=True
        )
        return model

    def _init_slurm_connection(self) -> None:
        """
        Sets up environment variables necessary for pytorch distributed communications
        based on slurm environment.
        """
        # use slurm job id for the port number
        # guarantees unique ports across jobs from same grid search
        try:
            # use the last 4 numbers in the job id as the id
            default_port = os.environ['SLURM_JOB_ID']
            default_port = default_port[-4:]

            # all ports should be in the 10k+ range
            default_port = int(default_port) + 15000

        except Exception:
            default_port = 12910

        # if user gave a port number, use that one instead
        try:
            default_port = os.environ['MASTER_PORT']
        except Exception:
            os.environ['MASTER_PORT'] = str(default_port)

        # figure out the root node addr
        try:
            root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
        except Exception:
            root_node = '127.0.0.1'

        root_node = self.trainer.resolve_root_node_address(root_node)
        os.environ['MASTER_ADDR'] = root_node

    def init_ddp_connection(
            self,
            proc_rank: int,
            world_size: int,
            is_slurm_managing_tasks: bool = True
    ) -> None:
        """
        Override to define your custom way of setting up a distributed environment.

        Lightning's implementation uses env:// init by default and sets the first node as root
        for SLURM managed cluster.

        Args:
            proc_rank: The current process rank within the node.
            world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).
            is_slurm_managing_tasks: is cluster managed by SLURM.

        """
        if is_slurm_managing_tasks:
            self._init_slurm_connection()

        if 'MASTER_ADDR' not in os.environ:
            log.warning("MASTER_ADDR environment variable is not defined. Set as localhost")
            os.environ['MASTER_ADDR'] = '127.0.0.1'
        log.debug(f"MASTER_ADDR: {os.environ['MASTER_ADDR']}")

        if 'MASTER_PORT' not in os.environ:
            log.warning("MASTER_PORT environment variable is not defined. Set as 12910")
            os.environ['MASTER_PORT'] = '12910'
        log.debug(f"MASTER_PORT: {os.environ['MASTER_PORT']}")

        if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:
            log.warning(f"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) "
                        f"is not equal to the computed world size ({world_size}). Ignored.")

        torch_backend = "nccl" if self.trainer.on_gpu else "gloo"
        log.info(f"initializing proc_rank {proc_rank} world {world_size}")
        torch_distrib.init_process_group(torch_backend, rank=proc_rank, world_size=world_size)

    def configure_apex(
            self,
            amp: object,
            model: 'LightningModule',
            optimizers: List[Optimizer],
            amp_level: str
    ) -> Tuple['LightningModule', List[Optimizer]]:
        r"""
        Override to init AMP your own way.
        Must return a model and list of optimizers.

        Args:
            amp: pointer to amp library object.
            model: pointer to current :class:`LightningModule`.
            optimizers: list of optimizers passed in :meth:`configure_optimizers`.
            amp_level: AMP mode chosen ('O1', 'O2', etc...)

        Return:
            Apex wrapped model and optimizers

        Examples:
            .. code-block:: python

                # Default implementation used by Trainer.
                def configure_apex(self, amp, model, optimizers, amp_level):
                    model, optimizers = amp.initialize(
                        model, optimizers, opt_level=amp_level,
                    )

                    return model, optimizers
        """
        model, optimizers = amp.initialize(
            model, optimizers, opt_level=amp_level,
        )

        return model, optimizers

    def configure_optimizers(self) -> Optional[Union[
        Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]
    ]]:
        r"""
        Choose what optimizers and learning-rate schedulers to use in your optimization.
        Normally you'd need one. But in the case of GANs or similar you might have multiple.

        Return:
            Any of these 6 options.

            - Single optimizer.
            - List or Tuple - List of optimizers.
            - Two lists - The first list has multiple optimizers, the second a list of LR schedulers.
            - Dictionary, with an 'optimizer' key and (optionally) a 'lr_scheduler' key.
            - Tuple of dictionaries as described, with an optional 'frequency' key.
            - None - Fit will run without any optimizer.

        Note:
            The 'frequency' value is an int corresponding to the number of sequential batches
            optimized with the specific optimizer. It should be given to none or to all of the optimizers.
            There is a difference between passing multiple optimizers in a list,
            and passing multiple optimizers in dictionaries with a frequency of 1:
            In the former case, all optimizers will operate on the given batch in each optimization step.
            In the latter, only one optimizer will operate on the given batch at every step.

        Examples:
            .. code-block:: python

                # most cases
                def configure_optimizers(self):
                    opt = Adam(self.parameters(), lr=1e-3)
                    return opt

                # multiple optimizer case (e.g.: GAN)
                def configure_optimizers(self):
                    generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
                    disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
                    return generator_opt, disriminator_opt

                # example with learning rate schedulers
                def configure_optimizers(self):
                    generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
                    disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
                    discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)
                    return [generator_opt, disriminator_opt], [discriminator_sched]

                # example with step-based learning rate schedulers
                def configure_optimizers(self):
                    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
                    dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
                    gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99),
                                 'interval': 'step'}  # called after each training step
                    dis_sched = CosineAnnealing(discriminator_opt, T_max=10) # called every epoch
                    return [gen_opt, dis_opt], [gen_sched, dis_sched]

                # example with optimizer frequencies
                # see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1
                # https://arxiv.org/abs/1704.00028
                def configure_optimizers(self):
                    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
                    dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
                    n_critic = 5
                    return (
                        {'optimizer': dis_opt, 'frequency': n_critic},
                        {'optimizer': gen_opt, 'frequency': 1}
                    )

        Note:

            Some things to know:

            - Lightning calls ``.backward()`` and ``.step()`` on each optimizer
              and learning rate scheduler as needed.

            - If you use 16-bit precision (``precision=16``), Lightning will automatically
              handle the optimizers for you.

            - If you use multiple optimizers, :meth:`training_step` will have an additional
              ``optimizer_idx`` parameter.

            - If you use LBFGS Lightning handles the closure function automatically for you.

            - If you use multiple optimizers, gradients will be calculated only
              for the parameters of current optimizer at each training step.

            - If you need to control how often those optimizers step or override the
              default ``.step()`` schedule, override the :meth:`optimizer_step` hook.

            - If you only want to call a learning rate scheduler every ``x`` step or epoch,
              or want to monitor a custom metric, you can specify these in a dictionary:

              .. code-block:: python

                  {
                      'scheduler': lr_scheduler,
                      'interval': 'step'  # or 'epoch'
                      'monitor': 'val_f1',
                      'frequency': x
                  }

        """
        rank_zero_warn('`configure_optimizers` must be implemented to be used with the Lightning Trainer')

    def optimizer_step(
            self,
            epoch: int,
            batch_idx: int,
            optimizer: Optimizer,
            optimizer_idx: int,
            second_order_closure: Optional[Callable] = None,
    ) -> None:
        r"""
        Override this method to adjust the default way the
        :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.
        By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example
        once per optimizer.

        Args:
            epoch: Current epoch
            batch_idx: Index of current batch
            optimizer: A PyTorch optimizer
            optimizer_idx: If you used multiple optimizers this indexes into that list.
            second_order_closure: closure for second order methods

        Examples:
            .. code-block:: python

                # DEFAULT
                def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                                   second_order_closure=None):
                    optimizer.step()
                    optimizer.zero_grad()

                # Alternating schedule for optimizer steps (i.e.: GANs)
                def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                                   second_order_closure=None):
                    # update generator opt every 2 steps
                    if optimizer_idx == 0:
                        if batch_idx % 2 == 0 :
                            optimizer.step()
                            optimizer.zero_grad()

                    # update discriminator opt every 4 steps
                    if optimizer_idx == 1:
                        if batch_idx % 4 == 0 :
                            optimizer.step()
                            optimizer.zero_grad()

                    # ...
                    # add as many optimizers as you want


            Here's another example showing how to use this for more advanced things such as
            learning rate warm-up:

            .. code-block:: python

                # learning rate warm-up
                def optimizer_step(self, current_epoch, batch_idx, optimizer,
                                    optimizer_idx, second_order_closure=None):
                    # warm up lr
                    if self.trainer.global_step < 500:
                        lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
                        for pg in optimizer.param_groups:
                            pg['lr'] = lr_scale * self.learning_rate

                    # update params
                    optimizer.step()
                    optimizer.zero_grad()

        Note:
            If you also override the :meth:`~pytorch_lightning.core.hooks.ModelHooks.on_before_zero_grad`
            model hook don't forget to add the call to it before ``optimizer.zero_grad()`` yourself.

        """
        if self.trainer.use_tpu and XLA_AVAILABLE:
            xm.optimizer_step(optimizer)
        elif isinstance(optimizer, torch.optim.LBFGS):

            # native amp + lbfgs is a no go right now
            if self.trainer.use_amp and self.trainer.use_native_amp:
                raise MisconfigurationException(
                    'native PyTorch amp and lbfgs are not compatible.'
                    ' To request, please file a Github issue in PyTorch and tag @mcarilli')
            optimizer.step(second_order_closure)
        else:
            if self.trainer.use_amp and self.trainer.use_native_amp:
                self.trainer.scaler.step(optimizer)
            else:
                optimizer.step()

        # in native 16-bit we need to update scaler after optimizer step
        if self.trainer.use_amp and self.trainer.use_native_amp:
            self.trainer.scaler.update()

        # model hook
        self.on_before_zero_grad(optimizer)

        # clear gradients
        optimizer.zero_grad()

    def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:
        r"""
        When using truncated backpropagation through time, each batch must be split along the
        time dimension. Lightning handles this by default, but for custom behavior override
        this function.

        Args:
            batch: Current batch
            split_size: The size of the split

        Return:
            List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated
            back propagation through time. The default implementation splits root level Tensors and
            Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.

        Examples:
            .. code-block:: python

                def tbptt_split_batch(self, batch, split_size):
                  splits = []
                  for t in range(0, time_dims[0], split_size):
                      batch_split = []
                      for i, x in enumerate(batch):
                          if isinstance(x, torch.Tensor):
                              split_x = x[:, t:t + split_size]
                          elif isinstance(x, collections.Sequence):
                              split_x = [None] * len(x)
                              for batch_idx in range(len(x)):
                                  split_x[batch_idx] = x[batch_idx][t:t + split_size]

                          batch_split.append(split_x)

                      splits.append(batch_split)

                  return splits

        Note:
            Called in the training loop after
            :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`
            if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.
            Each returned batch split is passed separately to :meth:`training_step`.

        """
        time_dims = [len(x[0]) for x in batch if isinstance(x, (torch.Tensor, collections.Sequence))]
        assert len(time_dims) >= 1, "Unable to determine batch time dimension"
        assert all(x == time_dims[0] for x in time_dims), "Batch time dimension length is ambiguous"

        splits = []
        for t in range(0, time_dims[0], split_size):
            batch_split = []
            for i, x in enumerate(batch):
                if isinstance(x, torch.Tensor):
                    split_x = x[:, t:t + split_size]
                elif isinstance(x, collections.Sequence):
                    split_x = [None] * len(x)
                    for batch_idx in range(len(x)):
                        split_x[batch_idx] = x[batch_idx][t:t + split_size]

                batch_split.append(split_x)

            splits.append(batch_split)

        return splits

    def prepare_data(self) -> None:
        """
        Use this to download and prepare data.
        In distributed (GPU, TPU), this will only be called once.
        This is called before requesting the dataloaders:

        .. code-block:: python

            model.prepare_data()
            model.train_dataloader()
            model.val_dataloader()
            model.test_dataloader()

        Examples:
            .. code-block:: python

                def prepare_data(self):
                    download_imagenet()
                    clean_imagenet()
                    cache_imagenet()
        """

    def train_dataloader(self) -> DataLoader:
        """
        Implement a PyTorch DataLoader for training.

        Return:
            Single PyTorch :class:`~torch.utils.data.DataLoader`.

        The dataloader you return will not be called every epoch unless you set
        :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.

        It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.

        - :meth:`~pytorch_lightning.trainer.Trainer.fit`
        - ...
        - :meth:`prepare_data`
        - :meth:`train_dataloader`

        Note:
            Lightning adds the correct sampler for distributed and arbitrary hardware.
            There is no need to set it yourself.

        Example:
            .. code-block:: python

                def train_dataloader(self):
                    transform = transforms.Compose([transforms.ToTensor(),
                                                    transforms.Normalize((0.5,), (1.0,))])
                    dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
                                    download=True)
                    loader = torch.utils.data.DataLoader(
                        dataset=dataset,
                        batch_size=self.batch_size,
                        shuffle=True
                    )
                    return loader

        """
        rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')

    def tng_dataloader(self):  # todo: remove in v1.0.0
        """
        Warnings:
            Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.
        """
        output = self.train_dataloader()
        rank_zero_warn("`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0."
                       " and this method will be removed in v1.0.0", DeprecationWarning)
        return output

    def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
        r"""
        Implement one or multiple PyTorch DataLoaders for testing.

        The dataloader you return will not be called every epoch unless you set
        :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.

        It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.

        - :meth:`~pytorch_lightning.trainer.Trainer.fit`
        - ...
        - :meth:`prepare_data`
        - :meth:`train_dataloader`
        - :meth:`val_dataloader`
        - :meth:`test_dataloader`

        Note:
            Lightning adds the correct sampler for distributed and arbitrary hardware.
            There is no need to set it yourself.

        Return:
            Single or multiple PyTorch DataLoaders.

        Example:
            .. code-block:: python

                def test_dataloader(self):
                    transform = transforms.Compose([transforms.ToTensor(),
                                                    transforms.Normalize((0.5,), (1.0,))])
                    dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
                                    download=True)
                    loader = torch.utils.data.DataLoader(
                        dataset=dataset,
                        batch_size=self.batch_size,
                        shuffle=False
                    )

                    return loader

        Note:
            If you don't need a test dataset and a :meth:`test_step`, you don't need to implement
            this method.

        """

    def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
        r"""
        Implement one or multiple PyTorch DataLoaders for validation.

        The dataloader you return will not be called every epoch unless you set
        :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.

        It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.

        - :meth:`~pytorch_lightning.trainer.Trainer.fit`
        - ...
        - :meth:`prepare_data`
        - :meth:`train_dataloader`
        - :meth:`val_dataloader`
        - :meth:`test_dataloader`

        Note:
            Lightning adds the correct sampler for distributed and arbitrary hardware
            There is no need to set it yourself.

        Return:
            Single or multiple PyTorch DataLoaders.

        Examples:
            .. code-block:: python

                def val_dataloader(self):
                    transform = transforms.Compose([transforms.ToTensor(),
                                                    transforms.Normalize((0.5,), (1.0,))])
                    dataset = MNIST(root='/path/to/mnist/', train=False,
                                    transform=transform, download=True)
                    loader = torch.utils.data.DataLoader(
                        dataset=dataset,
                        batch_size=self.batch_size,
                        shuffle=False
                    )

                    return loader

                # can also return multiple dataloaders
                def val_dataloader(self):
                    return [loader_a, loader_b, ..., loader_n]

        Note:
            If you don't need a validation dataset and a :meth:`validation_step`, you don't need to
            implement this method.

        Note:
            In the case where you return multiple validation dataloaders, the :meth:`validation_step`
            will have an argument ``dataset_idx`` which matches the order here.
        """

    @classmethod
    def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
        r"""
        Warning:
            Deprecated in version 0.7.0. You should use :meth:`load_from_checkpoint` instead.
            Will be removed in v0.9.0.
        """
        rank_zero_warn(
            "`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0."
            " The deprecated method will be removed in v0.9.0.", DeprecationWarning
        )
        return cls.load_from_checkpoint(weights_path, tags_csv=tags_csv, map_location=map_location)

    @classmethod
    def load_from_checkpoint(
            cls,
            checkpoint_path: str,
            *args,
            map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,
            hparams_file: Optional[str] = None,
            tags_csv: Optional[str] = None,  # backward compatible, todo: remove in v0.9.0
            **kwargs
    ) -> 'LightningModule':
        r"""
        Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint
        it stores the arguments passed to `__init__`  in the checkpoint under `module_arguments`

        Any arguments specified through \*args and \*\*kwargs will override args stored in `module_arguments`.

        Args:
            checkpoint_path: Path to checkpoint.
            args: Any positional args needed to init the model.
            map_location:
                If your checkpoint saved a GPU model and you now load on CPUs
                or a different number of GPUs, use this to map to the new setup.
                The behaviour is the same as in :func:`torch.load`.
            hparams_file: Optional path to a .yaml file with hierarchical structure
                as in this example::

                    drop_prob: 0.2
                    dataloader:
                        batch_size: 32

                You most likely won't need this since Lightning will always save the hyperparameters
                to the checkpoint.
                However, if your checkpoint weights don't have the hyperparameters saved,
                use this method to pass in a .yaml file with the hparams you'd like to use.
                These will be converted into a :class:`~dict` and passed into your
                :class:`LightningModule` for use.

                If your model's `hparams` argument is :class:`~argparse.Namespace`
                and .yaml file has hierarchical structure, you need to refactor your model to treat
                `hparams` as :class:`~dict`.

                .csv files are acceptable here till v0.9.0, see tags_csv argument for detailed usage.
            tags_csv:
                .. warning:: .. deprecated:: 0.7.6

                    `tags_csv` argument is deprecated in v0.7.6. Will be removed v0.9.0.

                Optional path to a .csv file with two columns (key, value)
                as in this example::

                    key,value
                    drop_prob,0.2
                    batch_size,32

                Use this method to pass in a .csv file with the hparams you'd like to use.
            hparam_overrides: A dictionary with keys to override in the hparams
            kwargs: Any keyword args needed to init the model.

        Return:
            :class:`LightningModule` with loaded weights and hyperparameters (if available).

        Example:
            .. code-block:: python

                # load weights without mapping ...
                MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')

                # or load weights mapping all weights from GPU 1 to GPU 0 ...
                map_location = {'cuda:1':'cuda:0'}
                MyLightningModule.load_from_checkpoint(
                    'path/to/checkpoint.ckpt',
                    map_location=map_location
                )

                # or load weights and hyperparameters from separate files.
                MyLightningModule.load_from_checkpoint(
                    'path/to/checkpoint.ckpt',
                    hparams_file='/path/to/hparams_file.yaml'
                )

                # override some of the params with new values
                MyLightningModule.load_from_checkpoint(
                    PATH,
                    num_layers=128,
                    pretrained_ckpt_path: NEW_PATH,
                )

                # predict
                pretrained_model.eval()
                pretrained_model.freeze()
                y_hat = pretrained_model(x)
        """
        if map_location is not None:
            checkpoint = torch.load(checkpoint_path, map_location=map_location)
        else:
            checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)

        # add the hparams from csv file to checkpoint
        if tags_csv is not None:
            hparams_file = tags_csv
            rank_zero_warn('`tags_csv` argument is deprecated in v0.7.6. Will be removed v0.9.0', DeprecationWarning)

        if hparams_file is not None:
            extension = hparams_file.split('.')[-1]
            if extension.lower() in ('csv'):
                hparams = load_hparams_from_tags_csv(hparams_file)
            elif extension.lower() in ('yml', 'yaml'):
                hparams = load_hparams_from_yaml(hparams_file)
            else:
                raise ValueError('.csv, .yml or .yaml is required for `hparams_file`')

            hparams['on_gpu'] = False

            # overwrite hparams by the given file
            checkpoint[CHECKPOINT_KEY_MODULE_ARGS] = hparams

        # override the module_arguments with values that were passed in
        checkpoint[CHECKPOINT_KEY_MODULE_ARGS].update(kwargs)

        model = cls._load_model_state(checkpoint, *args, **kwargs)
        return model

    @classmethod
    def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs) -> 'LightningModule':

        # pass in the values we saved automatically
        if CHECKPOINT_KEY_MODULE_ARGS in checkpoint:
            model_args = checkpoint[CHECKPOINT_KEY_MODULE_ARGS]
            kwargs.update(**model_args)

        # load the state_dict on the model automatically
        model = cls(*args, **kwargs)
        model.load_state_dict(checkpoint['state_dict'])

        # give model a chance to load something
        model.on_load_checkpoint(checkpoint)

        return model

    def summarize(self, mode: str) -> None:
        model_summary = ModelSummary(self, mode=mode)
        log.info('\n' + model_summary.__str__())

    def freeze(self) -> None:
        r"""
        Freeze all params for inference.

        Example:
            .. code-block:: python

                model = MyLightningModule(...)
                model.freeze()

        """
        for param in self.parameters():
            param.requires_grad = False

        self.eval()

    def unfreeze(self) -> None:
        """
        Unfreeze all parameters for training.

        .. code-block:: python

            model = MyLightningModule(...)
            model.unfreeze()

        """
        for param in self.parameters():
            param.requires_grad = True

        self.train()

    def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        r"""
        Called by Lightning to restore your model.
        If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.

        Args:
            checkpoint: Loaded checkpoint


        Example:
            .. code-block:: python

                def on_load_checkpoint(self, checkpoint):
                    # 99% of the time you don't need to implement this method
                    self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']

        Note:
            Lightning auto-restores global step, epoch, and train state including amp scaling.
            There is no need for you to restore anything regarding training.
        """

    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        r"""
        Called by Lightning when saving a checkpoint to give you a chance to store anything
        else you might want to save.

        Args:
            checkpoint: Checkpoint to be saved

        Example:
            .. code-block:: python


                def on_save_checkpoint(self, checkpoint):
                    # 99% of use cases you don't need to implement this method
                    checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object

        Note:
            Lightning saves all aspects of training (epoch, global step, etc...)
            including amp scaling.
            There is no need for you to store anything about training.

        """

    def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:
        r"""
        Additional items to be displayed in the progress bar.

        Return:
            Dictionary with the items to be displayed in the progress bar.
        """
        # call .item() only once but store elements without graphs
        running_train_loss = self.trainer.running_loss.mean()
        avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')
        tqdm_dict = {
            'loss': '{:.3f}'.format(avg_training_loss)
        }

        if self.trainer.truncated_bptt_steps is not None:
            tqdm_dict['split_idx'] = self.trainer.split_idx

        if self.trainer.logger is not None and self.trainer.logger.version is not None:
            tqdm_dict['v_num'] = self.trainer.logger.version

        return tqdm_dict

    def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:
        """
        Additional items to be displayed in the progress bar.

        Return:
            Dictionary with the items to be displayed in the progress bar.

        Warning:
            Deprecated since v0.7.3.
            Use :meth:`get_progress_bar_dict` instead.
        """
        rank_zero_warn("`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3"
                       " and this method will be removed in v1.0.0", DeprecationWarning)
        return self.get_progress_bar_dict()

    def _auto_collect_arguments(self):
        """Collect all arguments module arguments."""
        frame = inspect.currentframe()

        frame_args = _collect_init_args(frame.f_back, [])
        child = _get_latest_child(frame)

        # set module_arguments in child
        child._module_self_arguments = frame_args[-1]
        child._module_parents_arguments = {}
        for args in frame_args[:-1]:
            child._module_parents_arguments.update(args)

    @property
    def module_arguments(self) -> dict:
        """Aggregate this module and all parents arguments."""
        args = dict(self._module_parents_arguments)
        args.update(self._module_self_arguments)
        return args


def _collect_init_args(frame, path_args: list) -> list:
    """Recursive search for all children."""
    if '__class__' in frame.f_locals:
        local_args = dict(frame.f_locals)
        local_args.update(local_args.get('kwargs', {}))
        local_args = {k: v for k, v in local_args.items()
                      if k not in ('args', 'kwargs', 'self', '__class__', 'frame', 'frame_args')}
        # if 'hparams' in local_args:
        #     # back compatible hparams as single argument
        #     hparams = local_args.get('hparams')
        #     local_args.update(vars(hparams) if isinstance(hparams, Namespace) else hparams)
        # recursive update
        path_args.append(local_args)
        return _collect_init_args(frame.f_back, path_args)
    else:
        return path_args


def _get_latest_child(frame, child: object = None) -> object:
    """Recursive search for lowest child."""
    if 'self' in frame.f_locals:
        return _get_latest_child(frame.f_back, frame.f_locals['self'])
    else:
        return child