Change in logging frequency for automated logging

Bug description

Utilising Automated Logging with self.log and self.log_dict as described in the documentation results in a shift of the logging frequency after various amounts of steps.

This is also observed in all train metrics but only in the _step. It could be mutually exclusive to the WandbLogger but has already been observed to some extent in the TensorboardLogger as reported in #13525 and more generally in #10436.

How to reproduce the bug

Call self.log(, on_step=True, on_epoch=True) in training_step and let it run for more than 15k steps (in my case).
The logging rate initial is equal to log_every_n_steps=50 for some iterations but jumps wildly around for others.

batch_size=10 (specified in self.log(batch_size=10))
test_subjects=20
samples_per_subject=10

This equals to 200 samples per epoch and 20 steps per epoch. Even if log_every_n_steps=50, this should log then precisely every 100 steps (as the first 50 is not met according to my understanding) and not jump around from 2 to 200.

Environment

• Lightning Component: Trainer/ LightningModule
• Python 3.9
• Pytorch-lightning 1.9.1 (installed with pip)
• PyTorch 1.13.1
• CUDA/ cuDNN: cuda11.6-cudnn8
• OS: Linux (Kernel 3.10.0-1160)
• Running environment: server

Start with the Trainer/LightningModule automatic logging path around self.log, self.log_dict, training_step, and log_every_n_steps, then compare the WandbLogger and TensorboardLogger behavior described in the report. Reproduce the issue beyond 15k steps using the stated batch and epoch settings; done means _step logging remains at the expected frequency without irregular jumps.