Lots of different `nn.Sequence` instances trigger the Dynamo cache limits
Labels: high priority, triaged, has workaround, oncall: pt2, module: dynamo

### 🐛 Describe the bug

I am optimizing the training of an internal model. The entire forward call becomes 3x slower when enabling pytorch inductor. I've been profiling it a bit and turns out the lookup for the `check_fn` calls takes between 5ms and 10ms for each "Sequential.forward" call.

Turns out we have a lot of nn.Sequential instances in our model. All these container instances contain modules that are very different from each other. However, they all share the same "Sequence.forward" method that gets optimized by dynamo. This means, dynamo will try to optimize the method for each particular instance of the Sequential class and push those optimizations into the same cache list.

When it executes one `Sequential.forward` instance, it will have to iterate through all the cached `check_fn` functions by calling them one by one. There's an optimization in the `eval_frame.c:lookup` method that remembers the last `check_fn` that validated and optimizes for that particular call by moving it to the top of the list. 

However, chances are that same Sequential will not actually execute until the next training iteration, which means it will have to search through all other `check_fns` again for the next Sequential call.

To make matters worse, the check_fn has a lot of `Sequential[index]` calls, that sometimes look deep inside without caching the `Sequential[index]` values. That means, the check_fn ends up calling a lot of the `sequential.__getitem__`. Now if we analyze that method, it is not particularly cheap as the Sequential stores its list of modules in the "_modules" dict structure, so it has to iterate through all `dict.values()` to get the requested one.


### Error logs

Performance is 3x slower when inductor is enabled.

### Minified repro

_No response_

### Versions

```
Collecting environment information...
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.25.2
Libc version: glibc-2.31

Python version: 3.8.10 (default, Nov 14 2022, 12:59:47)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.13.0-1023-aws-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB
Nvidia driver version: 525.85.12
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.23.0
[pip3] open-clip-torch==2.11.1
[pip3] pytorch-lightning==1.9.0
[pip3] rotary-embedding-torch==0.1.5
[pip3] torch==2.0.0
[pip3] torchdata==0.5.1
[pip3] torchdistx==0.3.0.dev0+cu118
[pip3] torchmetrics==0.11.1
[pip3] torchvision==0.15.0a0+2d6e663
[conda] Could not collect
```

cc @ezyang @gchanan @zou3519 @msaroufim @wconstab @bdhirsh @anijain2305 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @soumith @ngimel @mlazos @yanboliang @desertfire