Repository: pytorch
Issue: inconsistency in ```torch.nn.functional.adaptive_avg_pool3d``` on CPU and GPU
Labels: module: nn,module: cuda,triaged

### 🐛 Describe the bug

consistency check of function ```torch.nn.functional.adaptive_avg_pool3d``` between CPU and GPU using a bfloat16 tensor.
```python 
import torch

input_tensor = torch.tensor([
    [
        [
            [-1.4062, 1.4609, 0.6797],
            [-0.6875, -0.9492, 0.4434],
            [-1.0312, -0.3730, 0.9453]
        ],
        [
            [0.9766, 0.2070, 0.8242],
            [-1.6484, 1.4531, 1.7891],
            [0.3945, 0.5352, -0.8711]
        ]
    ],
    [
        [
            [-2.5000, 0.2617, -0.3613],
            [-1.6094, -1.4219, -0.3281],
            [-1.3594, -2.3594, -0.5312]
        ],
        [
            [-1.9375, 1.0938, 1.5547],
            [-0.5820, -0.1167, 1.3438],
            [1.1953, -1.3750, -1.3438]
        ]
    ]
], dtype=torch.bfloat16)

output_size = (None, 1, None)

result_cpu = torch.nn.functional.adaptive_avg_pool3d(input_tensor, output_size)

input_cuda = input_tensor.cuda()
result_gpu = torch.nn.functional.adaptive_avg_pool3d(input_cuda, output_size)

print("CPU result:\n", result_cpu)
print("GPU result:\n", result_gpu.cpu())

inconsistent = not torch.allclose(result_cpu, result_gpu.cpu(), atol=1e-02, rtol=1e-03)
print("Inconsistency with atol=1e-02 and rtol=1e-03:", inconsistent)
```
outputs:
```
CPU result:
 tensor([[[[-1.0391,  0.0461,  0.6875]],

         [[-0.0923,  0.7305,  0.5781]]],


        [[[-1.8359, -1.1719, -0.4062]],

         [[-0.4395, -0.1328,  0.5195]]]], dtype=torch.bfloat16)

GPU result:
 tensor([[[[-1.0391,  0.0461,  0.6875]],

         [[-0.0923,  0.7305,  0.5820]]],


        [[[-1.8203, -1.1719, -0.4062]],

         [[-0.4414, -0.1328,  0.5195]]]], dtype=torch.bfloat16)

Inconsistency with atol=1e-02 and rtol=1e-03: True
```

### Versions

(executed on google colab)
PyTorch version: 2.5.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.30.5
Libc version: glibc-2.35

Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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):                               2
On-line CPU(s) list:                  0,1
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   2
Core(s) per socket:                   1
Socket(s):                            1
Stepping:                             3
BogoMIPS:                             4000.31
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 rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp 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 md_clear arch_capabilities
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            32 KiB (1 instance)
L1i cache:                            32 KiB (1 instance)
L2 cache:                             1 MiB (1 instance)
L3 cache:                             38.5 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0,1
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Mitigation; PTE Inversion
Vulnerability Mds:                    Vulnerable; SMT Host state unknown
Vulnerability Meltdown:               Vulnerable
Vulnerability Mmio stale data:        Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:             Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.3.3
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-nccl-cu12==2.23.4
[pip3] nvidia-nvjitlink-cu12==12.6.77
[pip3] nvtx==0.2.10
[pip3] optree==0.13.0
[pip3] pynvjitlink-cu12==0.4.0
[pip3] torch==2.5.0+cu121
[pip3] torchaudio==2.5.0+cu121
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.20.0+cu121
[conda] Could not collect

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki @ptrblck @msaroufim