Trainer crashed when optimizer frequency is defined. 
🐛 Bug

To Reproduce
Steps to reproduce the behavior:
Run the following code:
https://gist.github.com/24hours/ec67de5384bb05e28544d580ae424639

Traceback (most recent call last):
  File "pl_bug.py", line 40, in <module>
    trainer.fit(mnist_model, train_loader) 
  File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/states.py", line 48, in wrapped_fn
    result = fn(self, *args, **kwargs)
  File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1073, in fit
    results = self.accelerator_backend.train(model)
  File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/accelerators/gpu_backend.py", line 51, in train
    results = self.trainer.run_pretrain_routine(model)
  File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1239, in run_pretrain_routine
    self.train()
  File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 394, in train
    self.run_training_epoch()
  File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 491, in run_training_epoch
    batch_output = self.run_training_batch(batch, batch_idx)
  File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 883, in run_training_batch
    batch_outputs[opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)

Code sample
    def configure_optimizers(self):
        optimizer_G = torch.optim.Adam(self.parameters(), lr=0.1, weight_decay=1e-5)
        optimizer_D = torch.optim.Adam(self.parameters(), lr=0.1, weight_decay=1e-5)

        return [
                {'optimizer': optimizer_D, 'frequency': 5},
                {'optimizer': optimizer_G, 'frequency': 1}
            ]
the culprit is 'frequency' : 5, removing the line will allow trainer to run smoothly.
https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html?highlight=optimizers#configure-optimizers
The definition is correct according to this documentation.
Expected behavior
Model should train without crash.
The code work in 0.8.5 environment.
Environment

CUDA:

GPU:

GeForce GTX TITAN X


available:         True
version:           11.0


Packages:

numpy:             1.18.5
pyTorch_debug:     False
pyTorch_version:   1.6.0a0+9907a3e
pytorch-lightning: 0.9.0
tensorboard:       2.2.0
tqdm:              4.48.2


System:

OS:                Linux
architecture:

64bit



processor:         x86_64
python:            3.6.10
version:           #110-Ubuntu SMP Tue Jun 23 02:39:32 UTC 2020