Error occurs when overriding optimizer_step and using precision=16
🐛 Bug

To Reproduce
Steps to reproduce the behavior:

set precision=16 in Trainer
add optimizer_step method to LightningModule


Code sample

import os

import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision import transforms
import pytorch_lightning as pl

class MNISTModel(pl.LightningModule):

    def __init__(self):
        super(MNISTModel, self).__init__()
        self.l1 = torch.nn.Linear(28 * 28, 10)

    def forward(self, x):
        return torch.relu(self.l1(x.view(x.size(0), -1)))

    def training_step(self, batch, batch_nb):
        x, y = batch
        loss = F.cross_entropy(self(x), y)
        tensorboard_logs = {'train_loss': loss}
        return {'loss': loss, 'log': tensorboard_logs}

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.02)

    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure=None):
        optimizer.step()
        optimizer.zero_grad()

train_loader = DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)

mnist_model = MNISTModel()
trainer = pl.Trainer(gpus=1, precision=16, amp_level="O2")    
trainer.fit(mnist_model, train_loader)
Error
    raise RuntimeError("unscale_() has already been called on this optimizer since the last update().")
RuntimeError: unscale_() has already been called on this optimizer since the last update().

Expected behavior

Environment

CUDA:
- GPU:
- Tesla T4
- available:         True
- version:           10.2
Packages:
- numpy:             1.18.5
- pyTorch_debug:     False
- pyTorch_version:   1.6.0.dev20200622
- pytorch-lightning: 0.8.0
- tensorboard:       1.15.0
- tqdm:              4.46.1
System:
- OS:                Linux
- architecture:
- 64bit
-
- processor:         x86_64
- python:            3.7.7
- version:           #28~18.04.1-Ubuntu SMP Sat Jun 6 00:09:26 UTC 2020

Additional context