Track_grad_norm only tracks the parameters of the last optimizer defined
🐛 Bug
When you enable track_grad_norm in Trainer you expect it to track the grad of all the parameters defined in your lightning module. It seems like it only tracks the parameters for the last optimizer defined in def configure_optimizers().
To Reproduce
Steps to reproduce the behavior:

Create a pytorch lightning module with more than one optimizer
Enable logging to tensorboard and track_grad_norm in the trainer
Train model and observe only gradients for the last optimzer is tracked
Delete all tensorboard data and restart tensorboard
Train a model with the optimizers returned in the different order. Observe which gradients are tracked again


Code sample
import pytorch_lightning as pl
from torch.utils.data import TensorDataset, DataLoader
from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
from torch.optim import SGD
import torch.nn as nn
import torch

class MWENet(pl.LightningModule):
    def __init__(self):
        super(MWENet, self).__init__()
        
        self.first = nn.Conv2d(1, 1, 3)
        self.second = nn.Conv2d(1, 1, 3)
        self.loss = nn.L1Loss()
        
    def train_dataloader(self):
        xs, ys = torch.zeros(16, 1, 10, 10), torch.ones(16 ,1, 6, 6)
        ds = TensorDataset(xs, ys)
        return DataLoader(ds)
    
    def forward(self, xs):
        out = self.first(xs)
        out = self.second(out)
        return out
    
    def configure_optimizers(self):
        first = SGD(self.first.parameters(), lr=0.01)
        second = SGD(self.second.parameters(), lr=0.01)
        return [second, first]
    
    def training_step(self, batch, batch_idx, optimizer_idx):
        xs, ys = batch
        out = self.forward(xs)
        return {'loss': self.loss(out, ys)}

net = MWENet()
logger = TensorBoardLogger('somedir', name='testing')
trainer = pl.Trainer(track_grad_norm=2,
                     logger = logger)
trainer.fit(net)

Expected behavior
I expect that a pytorch module will report the gradient of all parameters when track_grad_norm is enabled.
Environment
cuda:
	GPU:
		GeForce RTX 2080 Ti
	available:           True
	version:             10.1
packages:
	numpy:               1.18.1
	pyTorch_debug:       False
	pyTorch_version:     1.4.0
	pytorch-lightning:   0.7.3
	tensorboard:         2.2.1
	tqdm:                4.45.0
system:
	OS:                  Windows
	architecture:
		64bit
		WindowsPE
	processor:           Intel64 Family 6 Model 158 Stepping 12, GenuineIntel
	python:              3.7.7
	version:             10.0.18362