JIT `lgamma` will return `inf` only with dual input in forward mode
Labels: oncall: jit

### 🐛 Describe the bug

JIT model with `lgamma` will return `inf` only with dual input in forward mode.

For any other input, it will return the correct value. It seems that there is an issue in the JIT and forward dual input

```py
import torch
def fn(input):
        input = torch.mul(input, torch.tensor(5, dtype=torch.float32, device='cuda'))
        input = torch.mul(input, torch.tensor(-16, dtype=torch.float32, device='cuda'))
        input = torch.div(input, torch.tensor(9, dtype=torch.float32, device='cuda'))
        fn_res = torch.lgamma(input, )
        return torch.add(fn_res, torch.tensor(2, dtype=torch.float32, device='cuda'))

torch.random.manual_seed(40277)
inp= torch.empty([3], dtype=torch.float16, memory_format=torch.contiguous_format)
inp.uniform_(-32, 63)
inp = inp.to('cuda')

def clone(inp):
    return inp.clone().requires_grad_()

print(fn(clone(inp)))

jit_fn = torch.jit.script(fn)
print(jit_fn(clone(inp)))

with torch.autograd.forward_ad.dual_level():
    print(jit_fn(clone(inp)))

    tan = torch.rand_like(inp)
    dual_inp = torch.autograd.forward_ad.make_dual(clone(inp), tan)
    print(jit_fn(dual_inp))
```

```
tensor([-2156.,  -738.,   621.], device='cuda:0', dtype=torch.float16,
       grad_fn=<AddBackward0>)
tensor([-2156.,  -738.,   621.], device='cuda:0', dtype=torch.float16,
       grad_fn=<AddBackward0>)
tensor([-2156.,  -738.,   621.], device='cuda:0', dtype=torch.float16,
       grad_fn=<AddBackward0>)
tensor([ inf,  inf, 621.], device='cuda:0', dtype=torch.float16,
       grad_fn=<AddBackward0>)
```



### Versions

```
PyTorch version: 1.13.0.dev20221001
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.2.0-19ubuntu1) 11.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.9.13 (main, Aug 25 2022, 23:26:10)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.5-051905-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.7.99
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060
Nvidia driver version: 515.65.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.23.3
[pip3] torch==1.13.0.dev20221001
[pip3] torchaudio==0.13.0.dev20221001
[pip3] torchvision==0.14.0.dev20221001
[conda] blas                      1.0                         mkl  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.23.3                   pypi_0    pypi
[conda] numpy-base                1.23.1           py39ha15fc14_0  
[conda] pytorch                   1.13.0.dev20221001 py3.9_cuda11.7_cudnn8.5.0_0    pytorch-nightly
[conda] pytorch-cuda              11.7                 h67b0de4_0    pytorch-nightly
[conda] pytorch-mutex             1.0                        cuda    pytorch-nightly
[conda] torch                     1.13.0.dev20220923+cu116          pypi_0    pypi
[conda] torchaudio                0.13.0.dev20220923+cu116          pypi_0    pypi
[conda] torchvision               0.14.0.dev20220923+cu116          pypi_0    pypi
```