Numbers bigger than the range should be inf while the implementation just keeps the original.
Labels: module: docs, triaged, module: NaNs and Infs

### 🐛 Describe the bug

Numbers bigger than the range should be inf while the implementation just keeps the original.

And I just wonder when we keep the original or just replace the number by inf.

```python
Python 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:18) 
[GCC 10.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.__version__
'1.11.0'
>>> torch.finfo(torch.float16)
finfo(resolution=0.001, min=-65504, max=65504, eps=0.000976562, tiny=6.10352e-05, dtype=float16)
>>> a = torch.tensor([65505.], dtype=torch.float16)
>>> a
tensor([65504.], dtype=torch.float16)
>>> b = torch.tensor([65570.], dtype=torch.float16)
>>> b
tensor([inf], dtype=torch.float16)
```

Thanks for any help in advance.

### Versions

```
Collecting environment information...
PyTorch version: 1.11.0
Is debug build: False
CUDA used to build PyTorch: 11.3
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: Could not collect
Libc version: glibc-2.31

Python version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:18)  [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 2080 Ti
GPU 1: NVIDIA GeForce RTX 2080 Ti

Nvidia driver version: 515.105.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
架构：                           x86_64
CPU 运行模式：                   32-bit, 64-bit
字节序：                         Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU:                             24
在线 CPU 列表：                  0-23
每个核的线程数：                 2
每个座的核数：                   12
座：                             1
NUMA 节点：                      1
厂商 ID：                        GenuineIntel
CPU 系列：                       6
型号：                           63
型号名称：                       Intel(R) Xeon(R) CPU E5-2678 v3 @ 2.50GHz
步进：                           2
CPU MHz：                        1200.000
CPU 最大 MHz：                   3300.0000
CPU 最小 MHz：                   1200.0000
BogoMIPS：                       4998.73
虚拟化：                         VT-x
L1d 缓存：                       384 KiB
L1i 缓存：                       384 KiB
L2 缓存：                        3 MiB
L3 缓存：                        30 MiB
NUMA 节点0 CPU：                 0-23
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          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; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
标记：                           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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx 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 cpuid_fault epb invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts md_clear flush_l1d

Versions of relevant libraries:
[pip3] numpy==1.22.3
[pip3] torch==1.11.0
[conda] _tflow_select             2.3.0                       mkl    defaults
[conda] blas                      1.0                         mkl    defaults
[conda] cudatoolkit               11.3.1               h2bc3f7f_2    defaults
[conda] libblas                   3.9.0            14_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            14_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            14_linux64_mkl    conda-forge
[conda] mkl                       2022.0.1           h06a4308_117    defaults
[conda] numpy                     1.19.2                   pypi_0    pypi
[conda] pytorch                   1.11.0          py3.8_cuda11.3_cudnn8.2.0_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] tensorflow                2.4.1           mkl_py38hb2083e0_0    defaults
[conda] tensorflow-base           2.4.1           mkl_py38h43e0292_0    defaults
```

cc @svekars @carljparker