`torch.compile` produce wrong result in `interpolate` when `mode=bilinear`
Labels: triaged, oncall: pt2, module: aotdispatch

### 🐛 Describe the bug

The following program prints different results in eager mode and compile mode. The difference exists only if `mode` is set to `bilinear`. 
```python
import torch

def fn(input):
    return torch.nn.functional.interpolate(input, size=[1, 1], mode="bilinear")
    # works fine if `mode` is set to other values such as "nearest" and "bicubic"

x = torch.rand([2, 8, 7, 10])
ret_eager = fn(x)

compiled = torch.compile(fn)
ret_compiled = compiled(x)

for r1, r2 in zip(ret_eager, ret_compiled):
    assert torch.allclose(r1, r2), (r1, r2)
print('==== Check OK! ====')

"""
Traceback (most recent call last):
  File "repro.py", line 14, in <module>
    assert torch.allclose(r1, r2), (r1, r2)
AssertionError: (
    tensor([[[0.5000]], [[0.5813]], [[0.5536]], [[0.2098]], [[0.3442]], [[0.7828]], [[0.6382]], [[0.3412]]]), 
    tensor([[[0.1909]], [[0.8426]], [[0.0925]], [[0.1988]], [[0.0879]], [[0.0518]], [[0.7094]], [[0.6926]]]))
"""
```

### Versions

<details><summary><b>Environment</b> <i>[Click to expand]</i></summary>

```
PyTorch version: 2.0.0.dev20230129+cu117
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 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 11.1.0-6
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.86.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.21.5
[pip3] pytorch-triton==2.0.0+0d7e753227
[pip3] torch==2.0.0.dev20230129+cu117
[pip3] torchaudio==2.0.0.dev20230118+cu117
[pip3] torchvision==0.15.0.dev20230118+cu117
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[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                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] torch                     1.14.0a0+gitce2f870          pypi_0    pypi
```

</details>

cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @mlazos @voznesenskym @yanboliang @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @chunyuan-w @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire