Behaviour when limit_train_batches is float is inconsistent
🐛 Bug
When passing limit_train_batches as a float to the trainer, the total number of steps during training is inconsistent and is dependent on accumulate_grad_batches, and independent of drop_last. Please see the tests in the notebooks for more examples.
Please reproduce using the BoringModel and post here

To Reproduce
See notebook.
https://colab.research.google.com/drive/1RmIwW97NM0bOmF2GUUilPyzCQ2MYYG1g?usp=sharing
Expected behavior
I don't have an expected behaviour other than to request that the total number of steps remain consistent. Either always drop an incomplete batch, or don't.
Environment

CUDA:

GPU:

Tesla T4


available:         True
version:           10.1


Packages:

numpy:             1.18.5
pyTorch_debug:     True
pyTorch_version:   1.7.0+cu101
pytorch-lightning: 1.0.8
tqdm:              4.41.1


System:

OS:                Linux
architecture:

64bit



processor:         x86_64
python:            3.6.9
version:           #1 SMP Thu Jul 23 08:00:38 PDT 2020



Additional context