Mixed precision: scheduler and optimizer are called in the wrong order

Nobody has claimed this yet.

## 🐛 Bug

When using mixed-precision training, scheduler and optimizer are called in the wrong order. Warning is generated:

```
UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.
```

## Please reproduce using the BoringModel

https://colab.research.google.com/drive/1G7pk6E9XUYq-pS41DXKhqM9Srx8sikiP?usp=sharing

There are four tests. Three of them doesn't raise the warning:

1. test_amp_scheduler(precision=16, configure_optimizers=configure_optimizers_1)
2. test_amp_scheduler(precision=32, configure_optimizers=configure_optimizers_1)
3. test_amp_scheduler(precision=32, configure_optimizers=configure_optimizers_2)

This testcase raises the warning:

1. test_amp_scheduler(precision=16, configure_optimizers=configure_optimizers_2)

### To Reproduce

1. Create model with `configure_optimizers` in a following dictionary style:

```python
def configure_optimizers_2(model):
optimizer = torch.optim.SGD(model.layer.parameters(), lr=0.1)
scheduler = {'scheduler': torch.optim.lr_scheduler.StepLR(optimizer, step_size=1),
'name': 'learning_rate',
'interval':'step',
'frequency': 1}

return {"optimizer": optimizer, "lr_scheduler": scheduler}
```

2. Enable mixed-precision training by setting `precision=16` in a `Trainer`
3. Start training

#### Note

When scheduler is defined in another way, the issue seems to not occur:

```python
def configure_optimizers_1(model):
optimizer = torch.optim.SGD(model.layer.parameters(), lr=0.1)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)

return {"optimizer": optimizer, "lr_scheduler": scheduler}
```

### Expected behavior

No warning

### Environment

* CUDA:
- GPU:
- Tesla P100-PCIE-16GB
- available: True
- version: 10.1
* Packages:
- numpy: 1.19.5
- pyTorch_debug: True
- pyTorch_version: 1.7.0+cu101
- pytorch-lightning: 1.1.4
- 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

cc @tchaton @rohitgr7 @carmocca @justusschock @awaelchli @akihironitta

Start with the four BoringModel test cases described in the issue, especially test_amp_scheduler with precision=16 and configure_optimizers_2. Compare the dictionary-style scheduler configuration with configure_optimizers_1 and trace the mixed-precision training flow. Done means the failing case no longer emits the optimizer-order warning while the other cases continue to pass.

Written by the indexing model from the issue text.