lightning/docs/source/tutorial_mnist.rst

Introduction Guide
==================
PyTorch Lightning provides a very simple template for organizing your PyTorch code. Once
you've organized it into a LightningModule, it automates most of the training for you.

To illustrate, here's the typical PyTorch project structure organized in a LightningModule.

.. figure:: /img/mnist/pt_to_pl.jpg
   :alt: mnist CPU bar

As your project grows in complexity with things like 16-bit precision, distributed training, etc... the part in blue
quickly becomes onerous and starts distracting from the core research code.

Goal of this guide
------------------
This guide walks through the major parts of the library to help you understand
what each parts does. But at the end of the day, you write the same PyTorch code... just organize it
into the LightningModule template which means you keep ALL the flexibility without having to deal with
any of the boilerplate code

To show how Lightning works, we'll start with an MNIST classifier and move into
a Variational Autoencoder and a Generative Adversarial Network (GAN).

.. note:: Any DL/ML PyTorch project fits into the Lightning structure. Here we just focus on 3 types
    of research to illustrate.

Lightning Philosophy
--------------------
Lightning factors DL/ML code into three types:

1. Core research code.
2. Engineering code.
3. Non-essential research code.

Research code
^^^^^^^^^^^^^
In the MNIST generation example, the research code would be the particular system and how it's trained (ie: A GAN or VAE).

In Lightning, this code is abstracted out by the `LightningModule`.

Engineering code
^^^^^^^^^^^^^^^^

The Engineering code is all the code related to training this system. Things such as early stopping, distribution
over GPUs, 16-bit precision, etc. This is normally code that is THE SAME across most projects.

In Lightning, this code is abstracted out by the `Trainer`.

Non-essential code
^^^^^^^^^^^^^^^^^^
This is code that helps the research but isn't relevant to the research code. Some examples might be:
1. Inspect gradients
2. Log to tensorboard.

In Lightning this code is abstracted out by `Callbacks`.

Elements of a research project
------------------------------
Every research project requires the same core ingredients:

1. A model
2. Train/val/test data
3. Optimizer(s)
4. Training step computations
5. Validation step computations
6. Test step computations


The Model
---------
The LightningModule provides the structure on how to organize these 5 ingredients.

Let's first start with the model. In this case we'll design
a 3-layer neural network.

.. code-block:: default

    import torch
    from torch.nn import functional as F
    from torch import nn
    import pytorch_lightning as pl

    class CoolMNIST(pl.LightningModule):

      def __init__(self):
        super(CoolMNIST, self).__init__()

        # mnist images are (1, 28, 28) (channels, width, height)
        self.layer_1 = torch.nn.Linear(28 * 28, 128)
        self.layer_2 = torch.nn.Linear(128, 256)
        self.layer_3 = torch.nn.Linear(256, 10)

      def forward(self, x):
        batch_size, channels, width, height = x.size()

        # (b, 1, 28, 28) -> (b, 1*28*28)
        x = x.view(batch_size, -1)

        # layer 1
        x = self.layer_1(x)
        x = torch.relu(x)

        # layer 2
        x = self.layer_2(x)
        x = torch.relu(x)

        # layer 3
        x = self.layer_3(x)

        # probability distribution over labels
        x = torch.log_softmax(x, dim=1)

        return x

Notice this is a `LightningModule` instead of a `torch.nn.Module`. A LightningModule is
equivalent to a PyTorch Module except it has added functionality. However, you can use it
EXACTLY the same as you would a PyTorch Module.

.. code-block:: default

    net = CoolMNIST()
    x = torch.Tensor(1, 1, 28, 28)
    out = net(x)

.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    torch.Size([1, 10])

Data
----

The Lightning Module organizes your dataloaders and data processing as well.
Here's the PyTorch code for loading MNIST

.. code-block:: default

    from torch.utils.data import DataLoader, random_split
    from torchvision.datasets import MNIST
    import os
    from torchvision import datasets, transforms


    # transforms
    # prepare transforms standard to MNIST
    transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

    # data
    mnist_train = MNIST(os.getcwd(), train=True, download=True)
    mnist_train = DataLoader(mnist_train, batch_size=64)

When using PyTorch Lightning, we use the exact same code except we organize it into
the LightningModule

.. code-block:: python

    from torch.utils.data import DataLoader, random_split
    from torchvision.datasets import MNIST
    import os
    from torchvision import datasets, transforms

    class CoolMNIST(pl.LightningModule):

      def train_dataloader(self):
        transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
        mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
        return DataLoader(mnist_train, batch_size=64)

Notice the code is exactly the same, except now the training dataloading has been organized by the LightningModule
under the `train_dataloader` method. This is great because if you run into a project that uses Lightning and want
to figure out how they prepare their training data you can just look in the `train_dataloader` method.

Optimizer
---------
Next we choose what optimizer to use for training our system.
In PyTorch we do it as follows:

.. code-block:: python

    from torch.optim import Adam
    optimizer = Adam(CoolMNIST().parameters(), lr=1e-3)


In Lightning we do the same but organize it under the configure_optimizers method.
If you don't define this, Lightning will automatically use `Adam(self.parameters(), lr=1e-3)`.

.. code-block:: python

    class CoolMNIST(pl.LightningModule):

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

Training step
-------------

The training step is what happens inside the training loop.

.. code-block:: python

    for epoch in epochs:
        for batch in data:
            # TRAINING STEP
            # ....
            # TRAINING STEP
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()

In the case of MNIST we do the following

.. code-block:: python

    for epoch in epochs:
        for batch in data:
            # TRAINING STEP START
            x, y = batch
            logits = model(x)
            loss = F.nll_loss(logits, y)
            # TRAINING STEP END

            loss.backward()
            optimizer.step()
            optimizer.zero_grad()

In Lightning, everything that is in the training step gets organized under the `training_step` function
in the LightningModule

.. code-block:: python

    class CoolMNIST(pl.LightningModule):

      def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self.forward(x)
        loss = F.nll_loss(logits, y)
        return {'loss': loss}
        # return loss (also works)

Again, this is the same PyTorch code except that it has been organized by the LightningModule.
This code is not restricted which means it can be as complicated as a full seq-2-seq, RL loop, GAN, etc...

Training
--------
So far we defined 4 key ingredients in pure PyTorch but organized the code inside the LightningModule.

1. Model.
2. Training data.
3. Optimizer.
4. What happens in the training loop.

For clarity, we'll recall that the full LightningModule now looks like this.

.. code-block:: python

    class CoolMNIST(pl.LightningModule):
      def __init__(self):
        super(CoolMNIST, self).__init__()
        self.layer_1 = torch.nn.Linear(28 * 28, 128)
        self.layer_2 = torch.nn.Linear(128, 256)
        self.layer_3 = torch.nn.Linear(256, 10)

      def forward(self, x):
        batch_size, channels, width, height = x.size()
        x = x.view(batch_size, -1)
        x = self.layer_1(x)
        x = torch.relu(x)
        x = self.layer_2(x)
        x = torch.relu(x)
        x = self.layer_3(x)
        x = torch.log_softmax(x, dim=1)
        return x

      def train_dataloader(self):
        transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
        mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
        return DataLoader(mnist_train, batch_size=64)

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

      def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self.forward(x)
        loss = F.nll_loss(logits, y)

        # add logging
        logs = {'loss': loss}
        return {'loss': loss, 'log': logs}

Again, this is the same PyTorch code, except that it's organized
by the LightningModule. This organization now lets us train this model

.. code-block:: python

    from pytorch_lightning import Trainer

    model = CoolMNIST()
    trainer = Trainer()
    trainer.fit(model)

You should see the following weights summary and progress bar

.. figure:: /img/mnist/mnist_cpu_bar.png
   :alt: mnist CPU bar

When we added the `log` key in the return dictionary it went into the built in tensorboard logger.
But you could have also logged by calling:

.. code-block:: python

    def training_step(self, batch, batch_idx):
        # ...
        loss = ...
        self.logger.summary.scalar('loss', loss)

Which will generate automatic tensorboard logs.

.. figure:: /img/mnist/mnist_tb.png
   :alt: mnist CPU bar


But the beauty is all the magic you can do with the trainer flags. For instance, to run this model on a GPU:

.. code-block:: python

    model = CoolMNIST()
    trainer = Trainer(gpus=1)
    trainer.fit(model)


.. figure:: /img/mnist/mnist_gpu.png
    :alt: mnist GPU bar

Or you can also train on multiple GPUs (not on colab though)

.. code-block:: python

    model = CoolMNIST()
    trainer = Trainer(gpus=8)
    trainer.fit(model)

Or multiple nodes

.. code-block:: python

    # (32 GPUs)
    model = CoolMNIST()
    trainer = Trainer(gpus=8, num_nodes=4, distributed_backend='ddp')
    trainer.fit(model)

And even TPUs. Let's do it on the colab!

First, change the runtime to TPU (and reinstall lightning).

.. figure:: /img/mnist/runtime_tpu.png
    :alt: mnist GPU bar

.. figure:: /img/mnist/restart_runtime.png
    :alt: mnist GPU bar

Next, install the required xla library (adds support for PyTorch on TPUs)

.. code-block:: default

    import collections
    from datetime import datetime, timedelta
    import os
    import requests
    import threading

    _VersionConfig = collections.namedtuple('_VersionConfig', 'wheels,server')
    VERSION = "torch_xla==nightly"  #@param ["xrt==1.15.0", "torch_xla==nightly"]
    CONFIG = {
        'xrt==1.15.0': _VersionConfig('1.15', '1.15.0'),
        'torch_xla==nightly': _VersionConfig('nightly', 'XRT-dev{}'.format(
            (datetime.today() - timedelta(1)).strftime('%Y%m%d'))),
    }[VERSION]
    DIST_BUCKET = 'gs://tpu-pytorch/wheels'
    TORCH_WHEEL = 'torch-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
    TORCH_XLA_WHEEL = 'torch_xla-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
    TORCHVISION_WHEEL = 'torchvision-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)

    # Update TPU XRT version
    def update_server_xrt():
      print('Updating server-side XRT to {} ...'.format(CONFIG.server))
      url = 'http://{TPU_ADDRESS}:8475/requestversion/{XRT_VERSION}'.format(
          TPU_ADDRESS=os.environ['COLAB_TPU_ADDR'].split(':')[0],
          XRT_VERSION=CONFIG.server,
      )
      print('Done updating server-side XRT: {}'.format(requests.post(url)))

    update = threading.Thread(target=update_server_xrt)
    update.start()

    # Install Colab TPU compat PyTorch/TPU wheels and dependencies
    !pip uninstall -y torch torchvision
    !gsutil cp "$DIST_BUCKET/$TORCH_WHEEL" .
    !gsutil cp "$DIST_BUCKET/$TORCH_XLA_WHEEL" .
    !gsutil cp "$DIST_BUCKET/$TORCHVISION_WHEEL" .
    !pip install "$TORCH_WHEEL"
    !pip install "$TORCH_XLA_WHEEL"
    !pip install "$TORCHVISION_WHEEL"
    !sudo apt-get install libomp5
    update.join()

In distributed training (multiple GPUs and multiple TPU cores) each GPU or TPU core will run a copy
of this program. This means that without taking any care you will download the dataset N times which
will cause all sorts of issues.

To solve this problem, move the download code to the `prepare_data` method in the LightningModule

.. code-block:: python

    class CoolMNIST(pl.LightningModule):
      def prepare_data(self):
        MNIST(os.getcwd(), train=True, download=True, transform=transform)

      def train_dataloader(self):
        transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
        mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
        return DataLoader(mnist_train, batch_size=64)

The `prepare_data` method is also a good place to do any data processing that needs to be done only
once (ie: download or tokenize, etc...).

.. note:: Lightning inserts the correct DistributedSampler for distributed training. No need to add yourself!

Now we can train the LightningModule on a TPU wihout doing anything else!

.. code-block:: python

    model = CoolMNIST()
    trainer = Trainer(num_tpu_cores=8)
    trainer.fit(model)

You'll now see the TPU cores booting up.

.. figure:: /img/mnist/tpu_start.png
    :alt: TPU start

Notice the epoch is MUCH faster!

.. figure:: /img/mnist/tpu_fast.png
    :alt: TPU speed

Validation loop
---------------
For most cases, we stop training the model when the performance on a validation
split of the data reaches a minimum.

Just like the `training_step`, we can define a `validation_step` to check whatever
metrics we care about, generate samples or add more to our logs.

.. code-block:: python

    for epoch in epochs:
        for batch in data:
            # ...
            # train

        # validate
        outputs = []
        for batch in val_data:
            x, y = batch                        # validation_step
            y_hat = model(x)                    # validation_step
            loss = loss(y_hat, x)               # validation_step
            outputs.append({'val_loss': loss})  # validation_step

        full_loss = outputs.mean()              # validation_end

Since the `validation_step` processes a single batch,
in Lightning we also have a `validation_end` method which allows you to compute
statistics on the full dataset and not just the batch.

In addition, we define a `val_dataloader` method which tells the trainer what data to use for validation.
Notice we split the train split of MNIST into train, validation. We also have to make sure to do the
sample split in the `train_dataloader` method.

.. code-block:: python

    class CoolMNIST(pl.LightningModule):
      def validation_step(self, batch, batch_idx):
        x, y = batch
        logits = self.forward(x)
        loss = F.nll_loss(logits, y)
        return {'val_loss': loss}

      def validation_end(self, outputs):
        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
        tensorboard_logs = {'val_loss': avg_loss}
        return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}

      def val_dataloader(self):
        transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
        mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
        _, mnist_val = random_split(mnist_train, [55000, 5000])
        mnist_val = DataLoader(mnist_val, batch_size=64)
        return mnist_val

Again, we've just organized the regular PyTorch code into two steps, the `validation_step` method which
operates on a single batch and the `validation_end` method to compute statistics on all batches.

If you have these methods defined, Lightning will call them automatically. Now we can train
while checking the validation set.

.. code-block:: python

    from pytorch_lightning import Trainer

    model = CoolMNIST()
    trainer = Trainer(num_tpu_cores=8)
    trainer.fit(model)

You may have noticed the words `Validation sanity check` logged. This is because Lightning runs 5 batches
of validation before starting to train. This is a kind of unit test to make sure that if you have a bug
in the validation loop, you won't need to potentially wait a full epoch to find out.

.. note:: Lightning disables gradients, puts model in eval mode and does everything needed for validation.

Testing loop
------------
Once our research is done and we're about to publish or deploy a model, we normally want to figure out
how it will generalize in the "real world." For this, we use a held-out split of the data for testing.

Just like the validation loop, we define exactly the same steps for testing:

- test_step
- test_end
- test_dataloader

.. code-block:: python

    class CoolMNIST(pl.LightningModule):
      def test_step(self, batch, batch_idx):
        x, y = batch
        logits = self.forward(x)
        loss = F.nll_loss(logits, y)
        return {'val_loss': loss}

      def test_end(self, outputs):
        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
        tensorboard_logs = {'val_loss': avg_loss}
        return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}

      def test_dataloader(self):
        transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
        mnist_train = MNIST(os.getcwd(), train=False, download=False, transform=transform)
        _, mnist_val = random_split(mnist_train, [55000, 5000])
        mnist_val = DataLoader(mnist_val, batch_size=64)
        return mnist_val

However, to make sure the test set isn't used inadvertently, Lightning has a separate API to run tests.
Once you train your model simply call `.test()`.

.. code-block:: python

    from pytorch_lightning import Trainer

    model = CoolMNIST()
    trainer = Trainer(num_tpu_cores=8)
    trainer.fit(model)

    # run test set
    trainer.test()

You can also run the test from a saved lightning model

.. code-block:: python

    model = CoolMNIST.load_from_checkpoint(PATH)
    trainer = Trainer(num_tpu_cores=8)
    trainer.test(model)

.. note:: Lightning disables gradients, puts model in eval mode and does everything needed for testing.

Predicting
----------
Again, a LightningModule is exactly the same as a PyTorch module. This means you can load it
and use it for prediction.

.. code-block:: python

    model = CoolMNIST.load_from_checkpoint(PATH)
    x = torch.Tensor(1, 1, 28, 28)
    out = model(x)

On the surface, it looks like `forward` and `training_step` are similar. Generally, we want to make sure that
what we want the model to do is what happens in the `forward`. whereas the `training_step` likely calls forward from
within it.

.. code-block:: python

    class CoolMNIST(pl.LightningModule):

      def forward(self, x):
        batch_size, channels, width, height = x.size()
        x = x.view(batch_size, -1)
        x = self.layer_1(x)
        x = torch.relu(x)
        x = self.layer_2(x)
        x = torch.relu(x)
        x = self.layer_3(x)
        x = torch.log_softmax(x, dim=1)
        return x

      def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self.forward(x)
        loss = F.nll_loss(logits, y)
        return loss

In this case, we've set this LightningModel to predict logits. But we could also have it predict feature maps:

.. code-block:: python

    class CoolMNIST(pl.LightningModule):

      def forward(self, x):
        batch_size, channels, width, height = x.size()
        x = x.view(batch_size, -1)
        x = self.layer_1(x)
        x1 = torch.relu(x)
        x = self.layer_2(x1)
        x2 = torch.relu(x)
        x3 = self.layer_3(x2)
        return [x, x1, x2, x3]

      def training_step(self, batch, batch_idx):
        x, y = batch
        out, l1_feats, l2_feats, l3_feats = self.forward(x)
        logits = torch.log_softmax(out, dim=1)
        ce_loss = F.nll_loss(logits, y)
        loss = perceptual_loss(l1_feats, l2_feats, l3_feats) + ce_loss
        return loss

How you split up what goes in `forward` vs `training_step` depends on how you want to use this model for
prediction.