loss from progress bar appears to be sum of loss across all GPUs in Lightning 1.0.3
🐛 Bug

The loss from progress bar during the training appears to be sum of loss across of all GPUs
Please reproduce using the BoringModel and post here

To Reproduce

One can pick any model and dataset and varies the number of GPUs for training. In my example, I have roughly 0.7 loss for 1 GPUs, 1.4 loss for 2 GPUs and 0.28 loss for 4 GPUs for the first few training batches, indicating loss is roughly the sum instead of mean of loss across all GPUs.
I used the standard from the doc:
def train_step(self, batch, batch_idx):
    loss = self.forward(batch)
   self.log('loss/train', loss, on_step=True, on_epoch=False, sync_dist=True, sync_dist_op='mean')


Note: the loss logged in tensorboard appears to be correct (mean of loss)
Expected behavior
The loss from progress bar during the training should be mean of loss across of all GPUs

Environment
Note: Bugs with code are solved faster ! Colab Notebook should be made public !


IDE: Please, use our python bug_report_model.py template.


Colab Notebook: Please copy and paste the output from our environment collection script (or fill out the checklist below manually).


You can get the script and run it with:
wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py
# For security purposes, please check the contents of collect_env_details.py before running it.
python collect_env_details.py


PyTorch Version (e.g., 1.0):  1.4
OS (e.g., Linux):  Linux
How you installed PyTorch (conda, pip, source):  pip
Build command you used (if compiling from source):
Python version:  3.7
CUDA/cuDNN version:  10.1
GPU models and configuration:
Any other relevant information:   PyTorch Lightning 1.0.3

Additional context