torch.jit.script'd function very slow on first invocation on latest nightly
Labels: oncall: jit, NNC, module: nvfuser

### 🐛 Describe the bug

It takes about a minute to run this function for the first time. It takes only a second if it's running on a version of PyTorch built from source.

To reproduce run the code in [this gist](https://gist.github.com/Linux-cpp-lisp/9ffccb39f5f0f192a0c07eb6645d32d3). All credit to finding this to @Linux-cpp-lisp. I suspect this is an environment issue, i.e. an old version of that we ship as a nightly vs. a newer version I'm using locally.

Clearly this prevents the optimization from being useful.

### Versions

The nightly in question here is 1.12.0.dev20220415-py3.9_cuda11.3_cudnn8.3.2_0

My local environment is 

```
Collecting environment information...
PyTorch version: 1.12.0a0+git075974e
Is debug build: False
CUDA used to build PyTorch: 11.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: 10.0.1
CMake version: version 3.22.3
Libc version: glibc-2.27

Python version: 3.9.5 (default, Jun  4 2021, 12:28:51)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.4.0-1051-aws-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: 11.1.105
GPU models and configuration: GPU 0: A100-SXM4-40GB
Nvidia driver version: 450.119.03
cuDNN version: Probably one of the following:
/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.5
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn.so.7.6.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy==0.812
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.20.3
[pip3] torch==1.12.0a0+git075974e
[pip3] torch2trt==0.3.0
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               11.3.1               h2bc3f7f_2
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-include               2021.2.0           h06a4308_296
[conda] mkl-random                1.2.1                    pypi_0    pypi
[conda] mkl-service               2.3.0                    pypi_0    pypi
[conda] mkl_fft                   1.3.0            py39h42c9631_2
[conda] mkl_random                1.2.2            py39h51133e4_0
[conda] mypy                      0.812              pyhd8ed1ab_0    conda-forge
[conda] mypy_extensions           0.4.3            py39h06a4308_0
[conda] numpy                     1.20.3                   pypi_0    pypi
[conda] numpy-base                1.20.2           py39hfae3a4d_0
[conda] torch                     1.11.0                   pypi_0    pypi
```

One notable difference is CUDA 11.1 locally vs 11.3 in the nightlies (note that the gist doesn't use CUDA).