fast_dev_run fail on log_hyperparams

[FredrikM97]

🐛 Bug

Issue when running: fast_dev_run=True
"TypeError: log_hyperparams() takes 2 positional arguments but 3 were given"

To Reproduce

When using the following: Where self.hp_metrics is a list of strings where each string is an available metric that is being logged, example "accuracy/val".

def on_train_start(self):
        if self.logger:
            self.logger.log_hyperparams(self.hparams, {metric:0 for metric in self.hp_metrics})

Expected behavior

Assume the unit test is wrong since the documentation say that self.logger.log_hyperparams takes one positional argument and one dictionary. The code run fine without fast_dev_run=True and everything is logged correctly to tensorboard.

Environment

pytorch_lightning 1.2.2

[awaelchli]

import torch
from torch.utils.data import Dataset

from pytorch_lightning import LightningModule, Trainer


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):
        output = self.layer(batch)
        return output.sum()

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

    def on_train_start(self):
        if self.logger:
            self.logger.log_hyperparams(self.hparams, {"x": 0})


if __name__ == '__main__':
    train_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
    model = BoringModel()
    trainer = Trainer(fast_dev_run=True)
    trainer.fit(model, train_data)

minimal repro example