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TPU training with PyTorch Lightning

  • Author: PL team

  • License: CC BY-SA

  • Generated: 2021-07-17T09:05:13.252067

In this notebook, we’ll train a model on TPUs. Updating one Trainer flag is all you need for that. The most up to documentation related to TPU training can be found here.


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Setup

This notebook requires some packages besides pytorch-lightning.

[ ]:
! pip install --quiet "torchvision" "torchmetrics>=0.3" "pytorch-lightning>=1.3" "torch>=1.6, <1.9"

Install Colab TPU compatible PyTorch/TPU wheels and dependencies

[ ]:
! pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl
[ ]:
import torch
import torch.nn.functional as F
from pytorch_lightning import LightningDataModule, LightningModule, Trainer
from torch import nn
from torch.utils.data import DataLoader, random_split
from torchmetrics.functional import accuracy
from torchvision import transforms
# Note - you must have torchvision installed for this example
from torchvision.datasets import MNIST

BATCH_SIZE = 1024

Defining The MNISTDataModule

Below we define MNISTDataModule. You can learn more about datamodules in docs.

[ ]:
class MNISTDataModule(LightningDataModule):

    def __init__(self, data_dir: str = './'):
        super().__init__()
        self.data_dir = data_dir
        self.transform = transforms.Compose([
            transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))
        ])

        # self.dims is returned when you call dm.size()
        # Setting default dims here because we know them.
        # Could optionally be assigned dynamically in dm.setup()
        self.dims = (1, 28, 28)
        self.num_classes = 10

    def prepare_data(self):
        # download
        MNIST(self.data_dir, train=True, download=True)
        MNIST(self.data_dir, train=False, download=True)

    def setup(self, stage=None):

        # Assign train/val datasets for use in dataloaders
        if stage == 'fit' or stage is None:
            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])

        # Assign test dataset for use in dataloader(s)
        if stage == 'test' or stage is None:
            self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)

    def train_dataloader(self):
        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE)

    def val_dataloader(self):
        return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=BATCH_SIZE)

Defining the LitModel

Below, we define the model LitMNIST.

[ ]:
class LitModel(LightningModule):

    def __init__(self, channels, width, height, num_classes, hidden_size=64, learning_rate=2e-4):

        super().__init__()

        self.save_hyperparameters()

        self.model = nn.Sequential(
            nn.Flatten(), nn.Linear(channels * width * height, hidden_size), nn.ReLU(), nn.Dropout(0.1),
            nn.Linear(hidden_size, hidden_size), nn.ReLU(), nn.Dropout(0.1),
            nn.Linear(hidden_size, num_classes)
        )

    def forward(self, x):
        x = self.model(x)
        return F.log_softmax(x, dim=1)

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

    def validation_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        acc = accuracy(preds, y)
        self.log('val_loss', loss, prog_bar=True)
        self.log('val_acc', acc, prog_bar=True)
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
        return optimizer

TPU Training

Lightning supports training on a single TPU core or 8 TPU cores.

The Trainer parameters tpu_cores defines how many TPU cores to train on (1 or 8) / Single TPU core to train on [1].

For Single TPU training, Just pass the TPU core ID [1-8] in a list. Setting tpu_cores=[5] will train on TPU core ID 5.

Train on TPU core ID 5 with tpu_cores=[5].

[ ]:
# Init DataModule
dm = MNISTDataModule()
# Init model from datamodule's attributes
model = LitModel(*dm.size(), dm.num_classes)
# Init trainer
trainer = Trainer(max_epochs=3, progress_bar_refresh_rate=20, tpu_cores=[5])
# Train
trainer.fit(model, dm)

Train on single TPU core with tpu_cores=1.

[ ]:
# Init DataModule
dm = MNISTDataModule()
# Init model from datamodule's attributes
model = LitModel(*dm.size(), dm.num_classes)
# Init trainer
trainer = Trainer(max_epochs=3, progress_bar_refresh_rate=20, tpu_cores=1)
# Train
trainer.fit(model, dm)

Train on 8 TPU cores with tpu_cores=8. You might have to restart the notebook to run it on 8 TPU cores after training on single TPU core.

[ ]:
# Init DataModule
dm = MNISTDataModule()
# Init model from datamodule's attributes
model = LitModel(*dm.size(), dm.num_classes)
# Init trainer
trainer = Trainer(max_epochs=3, progress_bar_refresh_rate=20, tpu_cores=8)
# Train
trainer.fit(model, dm)

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Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning movement, you can do so in the following ways!

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Great thanks from the entire Pytorch Lightning Team for your interest !

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