When calling trainer.test() train_dataloader is also validated, which makes no sense

[asusdisciple]

Bug description

In the current logic of pytorch-lightning everytime I call atrainer.test()it is also checked if the train_dataloader() function makes sense. This is problematic.

For example, I use a WeightedRandomSampler only in thetrain_dataloader for obvious reasons. In order for this to work I calculate the weights and num_samples parameters in the setup() stage="fit" section of my code.

Of course when I triggertrainer.test() this code is not executed and thus weights and num_samples are never calculated, which leads to an error when lightning validates thetrain_dataloader function.

I dont see any best practices to avoid this and no reason to validate code which is never executed.

What version are you seeing the problem on?

v2.2

How to reproduce the bug

No response

Error messages and logs

# Error messages and logs here please

Environment

Current environment

``` #- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow): #- PyTorch Lightning Version (e.g., 1.5.0): #- Lightning App Version (e.g., 0.5.2): #- PyTorch Version (e.g., 2.0): #- Python version (e.g., 3.9): #- OS (e.g., Linux): #- CUDA/cuDNN version: #- GPU models and configuration: #- How you installed Lightning(`conda`, `pip`, source): #- Running environment of LightningApp (e.g. local, cloud): ```

More info

No response

cc @justusschock @awaelchli

[carmocca]

This should not happen. Can you update the snippet below to show the problem?

import os

import torch
from lightning.pytorch import LightningModule, Trainer
from torch.utils.data import DataLoader, Dataset

class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len

class BoringModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def training_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("train_loss", loss)
        return {"loss": loss}

    def validation_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("valid_loss", loss)

    def test_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("test_loss", loss)

    def configure_optimizers(self):
        return torch.optim.SGD(self.layer.parameters(), lr=0.1)

    def train_dataloader(self):
        raise RuntimeError

    def val_dataloader(self):
        raise RuntimeError

    def test_dataloader(self):
        return DataLoader(RandomDataset(32, 64), batch_size=2)

def run():
    model = BoringModel()
    trainer = Trainer(
        default_root_dir=os.getcwd(),
        limit_train_batches=1,
        limit_val_batches=1,
        limit_test_batches=1,
        num_sanity_val_steps=0,
        max_epochs=1,
        enable_model_summary=False,
    )
    trainer.test(model)

if __name__ == "__main__":
    run()

[asusdisciple]

I found the bug. It appears when you use the strategy "deepspeed" in the trainer. Code below :)

import os

import torch

from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler
import lightning as L

class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len

class LDataset(L.LightningDataModule):
    def __init__(self):
        super().__init__()
        self.num_samples = None
        self.weights = None
        self.len = None
        self.train_data = None
        self.test_data = None

    def setup(self, stage: str):
        if stage == "fit":
            self.train_data = RandomDataset(32, 14)

            # since RandomSampler only balances train data, the weights are calculated here naturally
            self.weights = [1, 1, 1, 1, 1, 1]
            self.num_samples = len(self.train_data)
        if stage == "test":
            self.test_data = RandomDataset(32, 14)

    def train_dataloader(self):
        return DataLoader(self.train_data,
                          sampler=WeightedRandomSampler(replacement=True,
                                                        weights=self.weights,
                                                        num_samples=self.num_samples
                                                        ),
                          batch_size=2)

    def val_dataloader(self):
        raise RuntimeError

    def test_dataloader(self):
        return DataLoader(self.test_data, batch_size=2)

class BoringModel(L.LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def training_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("train_loss", loss)
        return {"loss": loss}

    def validation_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("valid_loss", loss)

    def test_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("test_loss", loss)

    def configure_optimizers(self):
        return torch.optim.SGD(self.layer.parameters(), lr=0.1)

def run():
    model = BoringModel()
    mydata = LDataset()
    trainer = L.Trainer(
        strategy="deepspeed",
        default_root_dir=os.getcwd(),
        limit_train_batches=1,
        limit_val_batches=1,
        limit_test_batches=1,
        num_sanity_val_steps=0,
        max_epochs=1,
        enable_model_summary=False,
        log_every_n_steps=10,
        enable_checkpointing=True,
        check_val_every_n_epoch=5
    )
    trainer.test(model, datamodule=mydata)

if __name__ == "__main__":
    run()