lightning/pytorch_lightning/core/lightning.py

import collections
import inspect
import logging as log
import os
import warnings
from abc import ABC, abstractmethod
from argparse import Namespace
from typing import Any, Callable, Dict, Optional, Union

import torch
import torch.distributed as dist
from torch.optim import Adam

from pytorch_lightning.core.decorators import data_loader
from pytorch_lightning.core.grads import GradInformation
from pytorch_lightning.core.hooks import ModelHooks
from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv
from pytorch_lightning.core.memory import ModelSummary
from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
from pytorch_lightning.utilities.debugging import MisconfigurationException

try:
    import torch_xla.core.xla_model as xm
    XLA_AVAILABLE = True

except ImportError:
    XLA_AVAILABLE = False


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

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

        #: Current dtype
        self.dtype = torch.FloatTensor

        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 your model is currently running on GPUs.
        #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.
        self.on_gpu = False

        #: 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 using amp
        self.use_amp = False

        self.hparams = None

    def print(self, *args, **kwargs):
        r"""
        Prints only from process 0. Use this in any distributed mode to log only once

        Args:
            x (object): The thing to print

        Examples:
        .. code-block:: python

            # example if we were using this model as a feature extractor
            def forward(self, x):
                self.print(x, 'in loader')

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

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

        Normally you'd call self.forward() from your 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:
            x (tensor): Whatever  you decide to define in the forward method

        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.forward(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):
        r"""return loss, dict with metrics for tqdm

        Args:
            batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your
                dataloader. A tensor, tuple or list
            batch_idx (int): Integer displaying index of this batch
            optimizer_idx (int): If using multiple optimizers, this argument will also be present.
            hiddens(:`Tensor <https://pytorch.org/docs/stable/tensors.html>`_):
                Passed in if truncated_bptt_steps > 0.

        Return:
            dict with loss key and optional log, progress keys
             if implementing 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.forward(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` param.

            .. code-block:: python

                # Multiple optimizers (ie: 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 hiddens from the previous truncated backprop step
                    ...
                    out, hiddens = self.lstm(data, hiddens)
                    ...

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

            You can also return a -1 instead of a dict to stop the current loop. This is useful
             if you want to break out of the current training epoch early.
        """

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

    def training_step_end(self, *args, **kwargs):
        """
        Use this when training with dp or ddp2 because 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:
            dictionary with loss key and optional log, progress 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)

        In this case you should define training_step_end to perform those calculations.

        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.forward(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.forward(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}

        .. seealso:: see the `multi-gpu guide for more details <multi_gpu.rst#caveats>`_.
        """

    def validation_step(self, *args, **kwargs):
        r"""
        Operate 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 (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your
                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 the validation_epoch_end

        .. 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)

        EExamples:
            .. code-block:: python

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

                    # implement your own
                    out = self.forward(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_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 validation_step is called, the model has been put in eval mode
            and PyTorch gradients have been disabled. At the end of validation,
            model goes back to training mode and gradients are enabled.
        """

    def validation_step_end(self, *args, **kwargs):
        """
        Use this when training with dp or ddp2 because 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]
            validation_step_end(batch_parts_outputs)

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

        Return:
            dictionary with loss key and optional log, progress 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)

        In this case you should define validation_step_end to perform those calculations.

        Examples:
            .. code-block:: python

                # WITHOUT validation_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.forward(x)
                    loss = self.softmax(out)
                    loss = nce_loss(loss)
                    return {'loss': loss}

                # --------------
                # with validation_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.forward(x)
                    return {'out': out}

                def validation_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}

        .. seealso:: see the `multi-gpu guide for more details <multi_gpu.rst#caveats>`_.
        """

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

    def validation_epoch_end(self, outputs: list):
        """
        Called at end of validation epoch with the output of all validation_steps

        .. code-block:: python

            # the pseudocode for these calls

            val_outs = []
            for val_batch in val_data:
                out = validation_step(train_batch)
                train_outs.append(out
            validation_epoch_end(val_outs)

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

        Return:
            Dict or OrderedDict (dict): 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 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_loss and 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):
        r"""
        Operate 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(train_batch)
                test_outs.append(out
            test_epoch_end(test_outs)

        Args:
            batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your
                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 test_epoch_end

        .. 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.forward(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_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, validation_step will have an additional
            argument.

        .. code-block:: python

            # CASE 2: multiple validation 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 validation_step is called, the model has been put in eval mode and
            PyTorch gradients have been disabled. At the end of validation, model goes back
            to training mode and gradients are enabled.
        """

    def test_step_end(self, *args, **kwargs):
        """
        Use this when training with dp or ddp2 because 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]
            test_step_end(batch_parts_outputs)

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

        Return:
            dictionary with loss key and optional log, progress 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)

        In this case you should define test_step_end to perform those calculations.

        Examples:
            .. code-block:: python

                # WITHOUT test_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.forward(x)
                    loss = self.softmax(out)
                    loss = nce_loss(loss)
                    return {'loss': loss}

                # --------------
                # with test_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.forward(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}

        .. seealso:: see the `multi-gpu guide for more details <multi_gpu.rst#caveats>`_.
        """

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

    def test_epoch_end(self, outputs):
        """
        Called at end of 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): List of outputs you defined in test_step, or if there are multiple
            dataloaders, a list containing a list of outputs for each dataloader

        Return:
            Dict or OrderedDict (dict): 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 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, device_ids):
        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 (:class:`.LightningModule`): the LightningModule currently being optimized
            device_ids (list): 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_ddp_connection(self, proc_rank, world_size):
        r"""

        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.

        Args:
            proc_rank (int): The current process rank within the node.
            world_size (int): Number of GPUs being use across all nodes. (num_nodes*nb_gpu_nodes).

        Examples:
            .. code-block:: python

                def init_ddp_connection(self):
                    # 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 as e:
                        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 = '127.0.0.2'

                    root_node = self.trainer.resolve_root_node_address(root_node)
                    os.environ['MASTER_ADDR'] = root_node
                    dist.init_process_group(
                        'nccl',
                        rank=self.proc_rank,
                        world_size=self.world_size
                    )

        """
        # 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 = '127.0.0.2'

        root_node = self.trainer.resolve_root_node_address(root_node)
        os.environ['MASTER_ADDR'] = root_node
        dist.init_process_group('nccl', rank=proc_rank, world_size=world_size)

    def configure_apex(self, amp, model, optimizers, amp_level):
        r"""
        Override to init AMP your own way
        Must return a model and list of optimizers

        Args:
            amp (object): pointer to amp library object
            model (:class:`.LightningModule`): pointer to current lightningModule
            optimizers (list): list of optimizers passed in configure_optimizers()
            amp_level (str): 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):
        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.

        If you don't define this method Lightning will automatically use Adam(lr=1e-3)

        Return: any of these 3 options:
            - Single optimizer
            - List or Tuple - List of optimizers
            - Two lists - The first list has multiple optimizers, the second a list of LR schedulers

        Examples:
            .. code-block:: python

                # most cases (default if not defined)
                def configure_optimizers(self):
                    opt = Adam(self.parameters(), lr=1e-3)
                    return opt

                # multiple optimizer case (eg: 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]

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

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

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

        .. note:: If you use LBFGS lightning handles the closure function automatically for you

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

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

        .. note:: If you only want to call a learning rate scheduler every `x` step or epoch,
            you can input this as 'frequency' key: dict(scheduler=lr_scheduler,
                                                        interval='step' or 'epoch', frequency=x)

        """
        return Adam(self.parameters(), lr=1e-3)

    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
        r"""

        Override this method to adjust the default way the Trainer calls each optimizer.
        By default, Lightning calls .step() and zero_grad() as shown in the example
        once per optimizer.

        Args:
            epoch (int): Current epoch
            batch_idx (int): Index of current batch
            optimizer (torch.nn.Optimizer): A PyTorch optimizer
            optimizer_idx (int): If you used multiple optimizers this indexes into that list
            second_order_closure (int): 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 (ie: 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.hparams.learning_rate

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

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

        # clear gradients
        optimizer.zero_grad()

    def tbptt_split_batch(self, batch, split_size):
        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 (torch.nn.Tensor): Current batch
            split_size (int): How big the split  is

        Return:
            list of batch splits. Each split will be passed to forward_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 on_batch_start if ``truncated_bptt_steps > 0``.
            Each returned batch split is passed separately to ``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):
        """Use this to download and prepare data.
        In distributed (GPU, TPU), this will only be called once

        Return:
            PyTorch DataLoader

        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()
        """
        return None

    def train_dataloader(self):
        """Implement a PyTorch DataLoader

        Return:
            PyTorch DataLoader

        Return a dataloader. It will not be called every epoch unless you set
        ```Trainer(reload_dataloaders_every_epoch=True)```.

        It's recommended that all data downloads and preparation happen in prepare_data().

        .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.
            No need to set yourself.

            - .fit()
            - ...
            - prepare_data()
            - train_dataloader

        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.hparams.batch_size,
                        shuffle=True
                    )
                    return loader

        """
        return None

    @data_loader
    def tng_dataloader(self):  # todo: remove in v1.0.0
        """Implement a PyTorch DataLoader.

        Warnings:
            Deprecated in v0.5.0. use train_dataloader instead. Will be removed 1.0.0
        """
        output = self.train_dataloader()
        warnings.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):
        r"""

        Return a dataloader. It will not be called every epoch unless you set
        ```Trainer(reload_dataloaders_every_epoch=True)```.

        It's recommended that all data downloads and preparation happen in prepare_data().

            - .fit()
            - ...
            - prepare_data()
            - train_dataloader
            - val_dataloader
            - test_dataloader

        .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.
            No need to set yourself.

        Return:
            PyTorch DataLoader

        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.hparams.batch_size,
                        shuffle=True
                    )

                    return loader

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

        .. note:: If you want to change the data during every epoch DON'T use the data_loader
            decorator.

        """
        return None

    def val_dataloader(self):
        r"""

        Return a dataloader. It will not be called every epoch unless you set
        ```Trainer(reload_dataloaders_every_epoch=True)```.

        It's recommended that all data downloads and preparation happen in prepare_data().

            - .fit()
            - ...
            - prepare_data()
            - train_dataloader
            - val_dataloader

        .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware
            No need to set yourself.

        Return:
            PyTorch DataLoader

        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.hparams.batch_size,
                        shuffle=True
                    )

                    return loader

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

            .. code-block:: python

                @pl.data_loader
                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.hparams.batch_size,
                        shuffle=True
                    )

                    return loader

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

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

        .. note:: If you want to change the data during every epoch DON'T use the data_loader
            decorator.

        .. note:: In the case where you return multiple `val_dataloaders`, the `validation_step`
            will have an argument `dataset_idx` which matches the order here.
        """
        return None

    @classmethod
    def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
        r"""
        Warning:
            Deprecated in version 0.7.0. You should use `load_from_checkpoint` instead.
             Will be removed in v0.9.0.
        """
        warnings.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,
            map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,
            tags_csv: Optional[str] = None,
    ) -> 'LightningModule':
        r"""

        Primary way of loading model from a checkpoint. When Lightning saves a checkpoint
        it stores the hyperparameters in the checkpoint if you initialized your LightningModule
        with an argument called `hparams` which is a Namespace (output of using argparse
        to parse command line arguments).

        Example:
            .. code-block:: python

                from argparse import Namespace
                hparams = Namespace(**{'learning_rate': 0.1})

                model = MyModel(hparams)

                class MyModel(LightningModule):
                    def __init__(self, hparams):
                        self.learning_rate = hparams.learning_rate

        Args:
            checkpoint_path: Path to checkpoint.
            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
                `torch.load <https://pytorch.org/docs/stable/torch.html#torch.load>`_.
            tags_csv: Optional path to a .csv file with two columns (key, value)
                as in this example::

                    key,value
                    drop_prob,0.2
                    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 .csv file with the hparams you'd like to use.
                These will be converted into a argparse.Namespace and passed into your
                LightningModule for use.

        Return:
            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',
                    tags_csv='/path/to/hparams_file.csv'
                )

                # 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)

        if tags_csv is not None:
            # add the hparams from csv file to checkpoint
            hparams = load_hparams_from_tags_csv(tags_csv)
            hparams.__setattr__('on_gpu', False)
            checkpoint['hparams'] = vars(hparams)

        model = cls._load_model_state(checkpoint)
        return model

    @classmethod
    def _load_model_state(cls, checkpoint):
        cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters
        ckpt_hparams = checkpoint.get('hparams')

        if cls_takes_hparams:
            if ckpt_hparams is not None:
                is_namespace = checkpoint.get('hparams_type') == 'namespace'
                hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams
            else:
                warnings.warn(
                    f"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__ "
                    f"contains argument 'hparams'. Will pass in an empty Namespace instead."
                    " Did you forget to store your model hyperparameters in self.hparams?"
                )
                hparams = Namespace()
        else:  # The user's LightningModule does not define a hparams argument
            if ckpt_hparams is None:
                hparams = None
            else:
                raise MisconfigurationException(
                    f"Checkpoint contains hyperparameters but {cls.__name__}'s __init__ "
                    f"is missing the  argument 'hparams'. Are you loading the correct checkpoint?"
                )

        # load the state_dict on the model automatically
        model_args = [hparams] if hparams else []
        model = cls(*model_args)
        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):
        model_summary = ModelSummary(self, mode=mode)
        log.info('\n' + model_summary.__str__())

    def freeze(self):
        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):
        """Unfreeze all params 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):
        r"""
        Called by lightning to restore your model.
        If you saved something with **on_save_checkpoint** this is your chance to restore this.

        Args:
            checkpoint (dict): 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:: Lighting auto-restores global step, epoch, and train state including amp scaling.
            No need for you to restore anything regarding training.
        """

    def on_save_checkpoint(self, checkpoint):
        r"""

        Called by lightning when saving a  checkpoint  to give you a chance to store anything
        else you might want to  save

        Args:
            checkpoint (dic): 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:: Lighting saves all aspects of training (epoch, global step, etc...)
            including amp scaling. No need
            for you to store anything about training.

        """

    def get_tqdm_dict(self):
        r"""
        Additional items to be displayed in the progress bar.

        Return:
            Dictionary with the items to be displayed in the progress bar.
        """
        tqdm_dict = {
            'loss': '{:.3f}'.format(self.trainer.avg_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