Repository: pytorch
Issue: Inconsistent behavior of `torch.tensor` in converting `NaN` to int32 on normal list input and numpy array, and different architectures.
Labels: module: error checking,triaged

### 🐛 Describe the bug

When converting `NaN` value to `int32`, the behavior of `torch.tensor` are different on different input type and different platform.
If the input is a python built-in list containing NaN value, there will be an exception. However, if the list is wrapped with numpy array, `torch.tensor` will sliently cast NaN to 0 (on macOS) and -2147483648 (on Linux).
```python
import torch
import numpy as np

numpy_array = np.array([np.nan], dtype=np.float64)

out = torch.tensor(numpy_array, dtype=torch.int32)
# tensor([0], dtype=torch.int32) on arm
# tensor([-2147483648], dtype=torch.int32) on x86
print(out)

normal_list = [np.nan]
out = torch.tensor(normal_list, dtype=torch.int32) # ValueError: cannot convert float NaN to integer
```

It seems that sliently casting NaN may cause trouble in debugging. If such checking is already implemented on normal list input, it may be helpful if the exception can be thrown on numpy array input, also making its behavior consistent on different OS.

### Versions

# MacOS:
Collecting environment information...
PyTorch version: 2.4.1
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 15.0.1 (arm64)
GCC version: Could not collect
Clang version: 16.0.0 (clang-1600.0.26.3)
CMake version: version 3.30.4
Libc version: N/A

Python version: 3.9.20 (main, Oct  3 2024, 02:24:59)  [Clang 14.0.6 ] (64-bit runtime)
Python platform: macOS-15.0.1-arm64-arm-64bit
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:
Apple M3 Max

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] optree==0.13.0
[pip3] torch==2.4.1
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] optree                    0.13.0                   pypi_0    pypi
[conda] torch                     2.4.1                    pypi_0    pypi

# Linux:
Collecting environment information...
PyTorch version: 2.4.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: AlmaLinux 9.4 (Seafoam Ocelot) (x86_64)
GCC version: (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)
Clang version: 16.0.1 (https://github.com/llvm/llvm-project.git cd89023f797900e4492da58b7bed36f702120011)
CMake version: version 3.23.2
Libc version: glibc-2.34

Python version: 3.9.18 (main, Aug 23 2024, 00:00:00)  [GCC 11.4.1 20231218 (Red Hat 11.4.1-3)] (64-bit runtime)
Python platform: Linux-5.14.0-427.37.1.el9_4.x86_64-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 11.2.67
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 2080 Ti
GPU 1: NVIDIA TITAN RTX
GPU 2: NVIDIA GeForce RTX 2080 Ti

Nvidia driver version: 555.42.02
cuDNN version: Could not collect
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
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             36
On-line CPU(s) list:                0-35
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 18
Socket(s):                          1
Stepping:                           7
CPU(s) scaling MHz:                 78%
CPU max MHz:                        4800.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           6000.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          576 KiB (18 instances)
L1i cache:                          576 KiB (18 instances)
L2 cache:                           18 MiB (18 instances)
L3 cache:                           24.8 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-35
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.4.1
[pip3] triton==3.0.0
[conda] Could not collect

cc @malfet @albanD @mruberry @rgommers @snadampal @milpuz01