Repository: pytorch
Issue: Segment fault on CPU and IndexError on CUDA for `_adaptive_avg_pool2d_backward`
Labels: module: crash,module: error checking,triaged,topic: fuzzer

### 🐛 Describe the bug

When calling the `torch.ops.aten._adaptive_avg_pool2d_backward` function with mismatched tensor dimensions, it causes a segmentation fault (SIGSEGV) on the CPU, but an `IndexError` on CUDA.

For exmple in CUDA:

```python
import torch

print(torch.__version__)

sym_0 = (1, 3, 8, 3)
sym_1 = torch.strided
sym_2 = 'cuda'
sym_3 = (1, 48)

v0 = torch.randn(size=sym_0, dtype=None, layout=sym_1, device=sym_2)
v1 = torch.rand(size=sym_3, device=sym_2)
torch.ops.aten._adaptive_avg_pool2d_backward(grad_output=v0, self=v1)
```

output:

```
2.7.0.dev20250116+cu124
Traceback (most recent call last):
  File "/home/yvesw/reborn2-expr/250117-bugs/test.py", line 12, in <module>
    torch.ops.aten._adaptive_avg_pool2d_backward(grad_output=v0, self=v1)
  File "/home/yvesw/miniconda3/envs/torch-preview/lib/python3.11/site-packages/torch/_ops.py", line 1158, in __call__
    return self._op(*args, **(kwargs or {}))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
IndexError: Dimension out of range (expected to be in range of [-2, 1], but got -3)
```

and in CPU:

```python
import torch

print(torch.__version__)

sym_0 = (1, 3, 8, 3)
sym_1 = torch.strided
sym_2 = 'cpu'
sym_3 = (1, 48)

v0 = torch.randn(size=sym_0, dtype=None, layout=sym_1, device=sym_2)
v1 = torch.rand(size=sym_3, device=sym_2)
torch.ops.aten._adaptive_avg_pool2d_backward(grad_output=v0, self=v1)
```

output:

```
2.7.0.dev20250116+cu124
fish: Job 3, 'python3 test.py' terminated by signal SIGSEGV (Address boundary error)
```

### Versions

```
Collecting environment information...
PyTorch version: 2.7.0.dev20250116+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Manjaro Linux (x86_64)
GCC version: (GCC) 14.2.1 20240805
Clang version: 18.1.8
CMake version: version 3.30.2
Libc version: glibc-2.40

Python version: 3.11.11 | packaged by conda-forge | (main, Dec  5 2024, 14:17:24) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-6.6.47-1-MANJARO-x86_64-with-glibc2.40
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4070
Nvidia driver version: 550.107.02
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.9.2.1
/usr/lib/libcudnn_adv.so.9.2.1
/usr/lib/libcudnn_cnn.so.9.2.1
/usr/lib/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/libcudnn_graph.so.9.2.1
/usr/lib/libcudnn_heuristic.so.9.2.1
/usr/lib/libcudnn_ops.so.9.2.1
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
Address sizes:                        39 bits physical, 48 bits virtual
字节序：                              Little Endian
CPU:                                  16
在线 CPU 列表：                       0-15
厂商 ID：                             GenuineIntel
型号名称：                            13th Gen Intel(R) Core(TM) i5-13400F
CPU 系列：                            6
型号：                                191
每个核的线程数：                      2
每个座的核数：                        10
座：                                  1
步进：                                2
CPU(s) scaling MHz:                   25%
CPU 最大 MHz：                        4600.0000
CPU 最小 MHz：                        800.0000
BogoMIPS：                            4993.00
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 art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
虚拟化：                              VT-x
L1d 缓存：                            416 KiB (10 instances)
L1i 缓存：                            448 KiB (10 instances)
L2 缓存：                             9.5 MiB (7 instances)
L3 缓存：                             20 MiB (1 instance)
NUMA 节点：                           1
NUMA 节点0 CPU：                      0-15
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.2
[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] pytorch-triton==3.2.0+git0d4682f0
[pip3] torch==2.7.0.dev20250116+cu124
[pip3] torchaudio==2.6.0.dev20250116+cu124
[pip3] torchvision==0.22.0.dev20250116+cu124
[conda] numpy                     2.1.2                    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] pytorch-triton            3.2.0+git0d4682f0          pypi_0    pypi
[conda] torch                     2.7.0.dev20250116+cu124          pypi_0    pypi
[conda] torchaudio                2.6.0.dev20250116+cu124          pypi_0    pypi
[conda] torchvision               0.22.0.dev20250116+cu124          pypi_0    pypi
```

cc @malfet