Repository: pytorch
Issue: `F.max_pool3d`: Segfault due to incorrect `dilation` check in kernel
Labels: high priority,triage review,module: crash,oncall: quantization

### 🐛 Describe the bug

`torch.nn.functional.max_pool3d` crashes for quantized input tensor and negative `dilation[0]`.

Test Code:
```Python3
import torch
input = torch.randn([1,1,1,1,1])
input = torch.quantize_per_tensor(input, 0.1, 10, torch.qint32)
result = torch.nn.functional.max_pool3d(input,(1,1,1),(1,1,1),(0,0,0),(-3,1,1))
```
Result:
```bash
Segmentation fault (core dumped)
```

Buggy Code:
https://github.com/pytorch/pytorch/blob/4d9d5fe540ce0524e94083f5a658f7d6d0b16aed/aten/src/ATen/native/quantized/cpu/Pooling.cpp#L369-L378 
In the kernel `q_maxpool_3d`, it dose not check if `dT` is positive. Therefore negative `dilation[0]` is not filtered out, and it ends up with segfault.

### Versions

Collecting environment information...
PyTorch version: 2.2.0a0+git00412e6
Is debug build: True
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 16.0.4 (https://github.com/llvm/llvm-project ae42196bc493ffe877a7e3dff8be32035dea4d07)
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.9.18 (main, Sep 11 2023, 13:41:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-100-generic-x86_64-with-glibc2.35
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:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             112
On-line CPU(s) list:                0-111
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz
CPU family:                         6
Model:                              106
Thread(s) per core:                 2
Core(s) per socket:                 28
Socket(s):                          2
Stepping:                           6
CPU max MHz:                        3500.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5200.00
Flags:                              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 pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
L1d cache:                          2.6 MiB (56 instances)
L1i cache:                          1.8 MiB (56 instances)
L2 cache:                           70 MiB (56 instances)
L3 cache:                           84 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-27,56-83
NUMA node1 CPU(s):                  28-55,84-111
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.2
[pip3] optree==0.10.0
[pip3] torch==2.4.0a0+git4d9d5fe
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-include               2023.1.0         h06a4308_46344  
[conda] numpy                     1.26.2                   pypi_0    pypi
[conda] optree                    0.10.0                   pypi_0    pypi
[conda] torch                     2.4.0a0+git4d9d5fe           dev_0    <develop>

cc @ezyang @gchanan @zou3519 @kadeng @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel @msaroufim @bdhirsh @anijain2305 @chauhang