lightning_optuna_multigpu example

pytorch_lightning_optuna.py

# https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_lightning_simple.py

"""
Optuna example that optimizes multi-layer perceptrons using PyTorch Lightning.
In this example, we optimize the validation accuracy of hand-written digit recognition using
PyTorch Lightning, and FashionMNIST. We optimize the neural network architecture. As it is too time
consuming to use the whole FashionMNIST dataset, we here use a small subset of it.
You can run this example as follows, pruning can be turned on and off with the `--pruning`
argument.
    $ python pytorch_lightning_simple.py [--pruning]
"""
import argparse
import os
from typing import List
from typing import Optional

from packaging import version
import pytorch_lightning as pl
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data import random_split
from torchvision import datasets
from torchvision import transforms

import optuna
from optuna.integration import PyTorchLightningPruningCallback


if version.parse(pl.__version__) < version.parse("1.0.2"):
    raise RuntimeError("PyTorch Lightning>=1.0.2 is required for this example.")

PERCENT_VALID_EXAMPLES = 0.1
BATCHSIZE = 128
CLASSES = 10
EPOCHS = 2
DIR = os.getcwd()


class Net(nn.Module):
    def __init__(self, dropout: float, output_dims: List[int]):
        super().__init__()
        layers: List[nn.Module] = []

        input_dim: int = 28 * 28
        for output_dim in output_dims:
            layers.append(nn.Linear(input_dim, output_dim))
            layers.append(nn.ReLU())
            layers.append(nn.Dropout(dropout))
            input_dim = output_dim

        layers.append(nn.Linear(input_dim, CLASSES))

        self.layers: nn.Module = nn.Sequential(*layers)

    def forward(self, data: torch.Tensor) -> torch.Tensor:
        logits = self.layers(data)
        return F.log_softmax(logits, dim=1)


class LightningNet(pl.LightningModule):
    def __init__(self, dropout: float, output_dims: List[int]):
        super().__init__()
        self.model = Net(dropout, output_dims)

    def forward(self, data: torch.Tensor) -> torch.Tensor:
        return self.model(data.view(-1, 28 * 28))
this
    def training_step(self, batch, batch_idx: int) -> torch.Tensor:
        data, target = batch
        output = self(data)
        return F.nll_loss(output, target)

    def validation_step(self, batch, batch_idx: int) -> None:
        data, target = batch
        output = self(data)
        pred = output.argmax(dim=1, keepdim=True)
        accuracy = pred.eq(target.view_as(pred)).float().mean()
        self.log("val_acc", accuracy)
        self.log("hp_metric", accuracy, on_step=False, on_epoch=True)

    def configure_optimizers(self) -> optim.Optimizer:
        return optim.Adam(self.model.parameters())


class FashionMNISTDataModule(pl.LightningDataModule):
    def __init__(self, data_dir: str, batch_size: int):
        super().__init__()
        self.data_dir = data_dir
        self.batch_size = batch_size

    def setup(self, stage: Optional[str] = None) -> None:
        self.mnist_test = datasets.FashionMNIST(
            self.data_dir, train=False, download=True, transform=transforms.ToTensor()
        )
        mnist_full = datasets.FashionMNIST(
            self.data_dir, train=True, download=True, transform=transforms.ToTensor()
        )
        self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])

    def train_dataloader(self) -> DataLoader:
        return DataLoader(
            self.mnist_train, batch_size=self.batch_size, shuffle=True, pin_memory=True
        )

    def val_dataloader(self) -> DataLoader:
        return DataLoader(
            self.mnist_val, batch_size=self.batch_size, shuffle=False, pin_memory=True
        )

    def test_dataloader(self) -> DataLoader:
        return DataLoader(
            self.mnist_test, batch_size=self.batch_size, shuffle=False, pin_memory=True
        )


def objective(trial: optuna.trial.Trial) -> float:

    # We optimize the number of layers, hidden units in each layer and dropouts.
    n_layers = trial.suggest_int("n_layers", 1, 3)
    dropout = trial.suggest_float("dropout", 0.2, 0.5)
    output_dims = [
        trial.suggest_int("n_units_l{}".format(i), 4, 128, log=True) for i in range(n_layers)
    ]

    model = LightningNet(dropout, output_dims)
    datamodule = FashionMNISTDataModule(data_dir=DIR, batch_size=BATCHSIZE)

    trainer = pl.Trainer(
        logger=True,
        limit_val_batches=PERCENT_VALID_EXAMPLES,
        checkpoint_callback=False,
        max_epochs=EPOCHS,
        gpus=1 if torch.cuda.is_available() else None,
        accelerator='ddp_spawn',
        callbacks=[PyTorchLightningPruningCallback(trial, monitor="val_acc")],
    )
    hyperparameters = dict(n_layers=n_layers, dropout=dropout, output_dims=output_dims)
    trainer.logger.log_hyperparams(hyperparameters)
    trainer.fit(model, datamodule=datamodule)

    return trainer.spawn_callback_metrics["val_acc"]
    #return trainer.spawn_callback_metrics["val_acc"]
    #return model.ddp_spawn_args.callback_metrics["val_acc"].item()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="PyTorch Lightning example.")
    parser.add_argument(
        "--pruning",
        "-p",
        action="store_true",
        help="Activate the pruning feature. `MedianPruner` stops unpromising "
        "trials at the early stages of training.",
    )
    args = parser.parse_args()

    pruner: optuna.pruners.BasePruner = (
        optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()
    )

    study = optuna.create_study(direction="maximize", pruner=pruner)
    study.optimize(objective, n_trials=100, timeout=600)

    print("Number of finished trials: {}".format(len(study.trials)))

    print("Best trial:")
    trial = study.best_trial

    print("  Value: {}".format(trial.value))

    print("  Params: ")
    for key, value in trial.params.items():
        print("    {}: {}".format(key, value))