Schedule in PyTorchProfiler doesn't work

🐛 Bug

The class pytorch_lightning.profiler.PyTorchProfiler doesn't work correctly with schedule parameter. It doesn't take into account the repeat parameter torch.profiler.schedule, which is very important in long-term learning.

To Reproduce

Reproduce with the BoringModel:

UPD: new link with example of main incorrect work and work with commit changes:

https://colab.research.google.com/drive/1UbbLx5N5Th0MsXu1olQwWqo7QLGd-lRY?usp=sharing

Expected behavior

I used pytorch_lightning.profiler.PyTorchProfiler with schedule=torch.profiler.schedule(wait=2, warmup=1, active=3, repeat=5,). And according to torch docs, i expect that there will be 5 cycles, each of which consists of 2 wait + 1 warmup + 3 active = 6 steps (per cycle), but in fact PyTorchProfiler records information about fewer cycles.

In code terms:

profiler = PyTorchProfiler(
        schedule=torch.profiler.schedule(
            wait=2, 
            warmup=1, 
            active=3, 
            repeat=5,
        ),
    )

    model = BoringModel()
    trainer = Trainer(
        max_epochs=1,
        profiler=profiler,
    )
    trainer.fit(model, train_dataloaders=train_data)

in this case the profiler should return information about 15 steps (3 active * 5 cylcles) to me, however it returns information about fewer steps because it doesn't record some cycles.

Environment

* CUDA:
	- GPU:
		- Tesla T4
	- available:         True
	- version:           11.3
* Packages:
	- lightning:         None
	- lightning_app:     None
	- numpy:             1.21.6
	- pyTorch_debug:     False
	- pyTorch_version:   1.12.0+cu113
	- pytorch-lightning: 1.7.0
	- tqdm:              4.64.0
* System:
	- OS:                Linux
	- architecture:
		- 64bit
		- 
	- processor:         x86_64
	- python:            3.7.13
	- version:           #1 SMP Sun Apr 24 10:03:06 PDT 2022

Additional context

I found the same issue #12611 .

cc @carmocca @kaushikb11 @ninginthecloud @rohitgr7 @nbcsm @guotuofeng