Repository: pytorch
Issue: interpolate::trilinear has different result between 1.8.0 and the newer version
Labels: module: nn,triaged

### 🐛 Describe the bug

```python
import numpy as np
import torch

# use numpy to generate data
input = torch.from_numpy(np.random.uniform(np.finfo(np.float32).min, np.finfo(np.float32).max, (20, 20, 20, 20, 20)).astype(np.float32))
# call two version api
# 1.8 version
out = torch.nn.functional.interpolate(input, scale_factor=2.0, mode='trilinear', align_corners=None, recompute_scale_factor=None)
# 1.11 version
#out = torch.nn.functional.interpolate(input, scale_factor=2.0, mode='trilinear', align_corners=None, recompute_scale_factor=None, antialias=None)
```
===== compare the result between the different version ========
```python
print(torch.isclose(result_1_11.flatten(), result_1_8.flatten()).all())
# >>> tensor(False)
```
And the result between 1.8.0 and 1.11.0 is different, when input is large. Is this expected?

### Versions

Collecting environment information...
PyTorch version: 1.8.0
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.17

Python version: 3.7.16 (default, Jan 17 2023, 22:20:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.10.47-linuxkit-x86_64-with-debian-bookworm-sid
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
Address sizes:                   39 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          6
On-line CPU(s) list:             0-5
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Core(TM) i7-1068NG7 CPU @ 2.30GHz
CPU family:                      6
Model:                           126
Thread(s) per core:              1
Core(s) per socket:              1
Socket(s):                       6
Stepping:                        5
BogoMIPS:                        4600.00
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 pbe syscall nx pdpe1gb lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid pni pclmulqdq dtes64 ds_cpl ssse3 sdbg fma cx16 xtpr pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch fsgsbase bmi1 avx2 bmi2 erms avx512f avx512cd xsaveopt arat
L1d cache:                       288 KiB (6 instances)
L1i cache:                       192 KiB (6 instances)
L2 cache:                        3 MiB (6 instances)
L3 cache:                        48 MiB (6 instances)
Vulnerability Itlb multihit:     KVM: Mitigation: VMX unsupported
Vulnerability L1tf:              Mitigation; PTE Inversion
Vulnerability Mds:               Vulnerable; SMT Host state unknown
Vulnerability Meltdown:          Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, STIBP: disabled
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.21.6
[pip3] torch==1.8.0
[conda] numpy                     1.21.6                   pypi_0    pypi
[conda] torch                     1.8.0                    pypi_0    pypi

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki