Repository: pytorch
Issue: [Inductor] Different results with Conv2d and BN2d not in `eval mode`
Labels: triaged,oncall: pt2,module: inductor

### 🐛 Describe the bug

If I use Inductor to compile `Conv2d` and `BN2d` **not use** `eval mode`. The results are inconsistent.
The problem seems to be with BN2d

```python
import torch
import torch.nn as nn


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(3, 16, kernel_size=3, padding=2, dilation=2, groups=1)
        self.bn = nn.BatchNorm2d(16)
        self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.global_avg_pool(x)
        return x


m = Model()

x = torch.randn(1, 3, 128, 128)  # With the increase of height and width, the error becomes more obvious


output = m(x)
compiled_model = torch.compile(m)

c_output = compiled_model(x)

print(output)
print(c_output)
res = torch.allclose(output, c_output)
print(res)

```

<details>
<summary>click here to see the err log</summary>

```
tensor([[[[-8.6147e-09]],

         [[ 9.8953e-10]],

         [[-3.4925e-10]],

         [[ 4.0745e-09]],

         [[-3.9581e-09]],

         [[-1.3970e-09]],

         [[ 1.2107e-08]],

         [[ 4.6566e-10]],

         [[-2.2119e-09]],

         [[ 5.5879e-09]],

         [[ 9.5170e-09]],

         [[-6.9849e-10]],

         [[ 1.1525e-08]],

         [[-1.6298e-09]],

         [[-2.1537e-09]],

         [[ 5.1223e-09]]]], grad_fn=<MeanBackward1>)

```

```
tensor([[[[ 2.1268e-08]],

         [[ 5.0095e-08]],

         [[ 4.3936e-08]],

         [[-1.0681e-07]],

         [[-5.3694e-07]],

         [[-1.0622e-07]],

         [[-1.0726e-06]],

         [[-9.5417e-08]],

         [[ 4.2513e-07]],

         [[-8.6298e-08]],

         [[ 2.0579e-07]],

         [[ 2.8708e-08]],

         [[ 1.4902e-07]],

         [[-5.8193e-08]],

         [[-1.7331e-07]],

         [[-2.9289e-08]]]], grad_fn=<CompiledFunctionBackward>)
```

```
False
```

</details>

If there is a problem with my use, feel free to let me know :)

### Versions

<details>
<summary>click here to view the version env</summary>
Collecting environment information...
PyTorch version: 2.6.0.dev20241115+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: 16.0.1
CMake version: version 3.26.0
Libc version: glibc-2.31

Python version: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-200-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.6.68
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-32GB
GPU 1: Tesla V100-SXM2-32GB
GPU 2: Tesla V100-SXM2-32GB
GPU 3: Tesla V100-SXM2-32GB

Nvidia driver version: 560.35.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.4.0
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:                      40 bits physical, 48 bits virtual
CPU(s):                             20
On-line CPU(s) list:                0-19
Thread(s) per core:                 1
Core(s) per socket:                 20
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              85
Model name:                         Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz
Stepping:                           7
CPU MHz:                            2499.994
BogoMIPS:                           4999.98
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          640 KiB
L1i cache:                          640 KiB
L2 cache:                           80 MiB
L3 cache:                           16 MiB
NUMA node0 CPU(s):                  0-19
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:        KVM: Vulnerable
Vulnerability L1tf:                 Mitigation; PTE Inversion
Vulnerability Mds:                  Vulnerable: Clear CPU buffers attempted, no microcode; 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 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 disabled; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Vulnerable: Clear CPU buffers attempted, no microcode; 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 arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch topoext cpuid_fault invpcid_single pti ssbd ibrs ibpb fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat umip pku ospke avx512_vnni

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] optree==0.13.1
[pip3] pytorch-triton==3.1.0+cf34004b8a
[pip3] torch==2.6.0.dev20241115+cu124
[pip3] torchaudio==2.5.0.dev20241115+cu124
[pip3] torchvision==0.20.0.dev20241115+cu124
[pip3] triton==3.0.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.4.5.8                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.4.127                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.2.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.5.147               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.6.1.9                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.3.1.170               pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.6.2                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.4.127                 pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.4.127                 pypi_0    pypi
[conda] optree                    0.13.1                   pypi_0    pypi
[conda] pytorch-triton            3.1.0+cf34004b8a          pypi_0    pypi
[conda] torch                     2.6.0.dev20241115+cu124          pypi_0    pypi
[conda] torchaudio                2.5.0.dev20241115+cu124          pypi_0    pypi
[conda] torchvision               0.20.0.dev20241115+cu124          pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
</details>

cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @aakhundov