Repository: pytorch
Issue: Cannot load a jit-traced model if it uses nan_to_num with too large a constant.
Labels: oncall: jit

### 🐛 Describe the bug

The function `nan_to_num` requires a float and not an int when tracing. This creates a problem if you use too large of a constant, which I assume is outside float range or something for this bug to make sense. Instead of interpreting the number as a float, it is instead interpreted as a constant if you jit.save and then jit.load the model.

```
from io import BytesIO
import torch

def func(x):
    return x.nan_to_num(1e10)

m = torch.jit.trace(func, torch.ones((3, 3)))
buffer = BytesIO()
torch.jit.save(m, buffer)
buffer.seek(0)
m = torch.jit.load(buffer)
```

Error message:
```
Traceback (most recent call last):
  File "/my_project/test.py", line 14, in <module>
    m = torch.jit.load(buffer)
  File "/home/anaconda3/envs/my_project/lib/python3.10/site-packages/torch/jit/_serialization.py", line 164, in load
    cpp_module = torch._C.import_ir_module_from_buffer(
RuntimeError: 
Arguments for call are not valid.
The following variants are available:
  
  aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor:
  Expected a value of type 'Optional[float]' for argument 'nan' but instead found type 'int'.
  
  aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!):
  Expected a value of type 'Optional[float]' for argument 'nan' but instead found type 'int'.

The original call is:
  File "code/__torch__.py", line 7
  def forward(self: __torch__.PlaceholderModule,
    x: Tensor) -> Tensor:
    return torch.nan_to_num(x, 10000000000)
           ~~~~~~~~~~~~~~~~ <--- HERE
```

Note that this does not happen if you use `1e9` as the constant instead.

### Versions

Collecting environment information...
PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.26.3
Libc version: glibc-2.31

Python version: 3.10.11 (main, Apr 20 2023, 19:02:41) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1047-gcp-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 48 bits virtual
CPU(s):                             8
On-line CPU(s) list:                0-7
Thread(s) per core:                 2
Core(s) per socket:                 4
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              79
Model name:                         Intel(R) Xeon(R) CPU @ 2.20GHz
Stepping:                           0
CPU MHz:                            2200.218
BogoMIPS:                           4400.43
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          128 KiB
L1i cache:                          128 KiB
L2 cache:                           1 MiB
L3 cache:                           55 MiB
NUMA node0 CPU(s):                  0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Mitigation; PTE Inversion
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:             Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT Host state unknown
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities

Versions of relevant libraries:
[pip3] mypy==1.2.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.3
[pip3] pytorch-lightning==2.0.2
[pip3] torch==2.1.2
[pip3] torchmetrics==0.11.4
[pip3] triton==2.1.0
[conda] numpy                     1.24.3                   pypi_0    pypi
[conda] pytorch-lightning         2.0.2                    pypi_0    pypi
[conda] torch                     2.1.2                    pypi_0    pypi
[conda] torchmetrics              0.11.4                   pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypi

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel