Repository: pytorch
Issue: ```torch.nn.Upsample``` class: Internal assert failed
Labels: oncall: quantization

### 🐛 Describe the bug

Test code:
```python
import torch

base = torch.randn([1,1,1,1,1])
temp_input = torch.quantize_per_tensor(base, 0.1, 10, torch.quint2x4)

module = torch.nn.Upsample(scale_factor=(1e-5,1e-6,0.25), mode='nearest')
module(temp_input)
```

Error log:
```
Traceback (most recent call last):
  File "/home/yonghyeon/pytorch_err_case_py/upsample.py", line 7, in <module>
    module(temp_input)
  File "/home/yonghyeon/pytorch/pytorch-asan/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yonghyeon/pytorch/pytorch-asan/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yonghyeon/pytorch/pytorch-asan/torch/nn/modules/upsampling.py", line 157, in forward
    return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yonghyeon/pytorch/pytorch-asan/torch/nn/functional.py", line 4030, in interpolate
    return torch._C._nn.upsample_nearest3d(input, output_size, scale_factors)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: input_width > 0 && output_width > 0 INTERNAL ASSERT FAILED at "/home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/native/quantized/cpu/UpSampleNearest3d.cpp":158, please report a bug to PyTorch.
```

Test code:
```cpp
#include <torch/torch.h>

int main() {
    auto base = torch::randn({1,1,1,1,1});
    torch::Tensor input = at::quantize_per_tensor(base, 0.1, 10, c10::kQUInt2x4);

    c10::optional<std::vector<double>> scale_factor = c10::optional<std::vector<double>>({1e-5,1e-6,0.25});

    auto options_ = 
        torch::nn::UpsampleOptions()
        .size(c10::nullopt)
        .scale_factor(scale_factor)
        .mode(torch::kNearest)
        .align_corners(c10::nullopt);

    auto module = torch::nn::Upsample(options_);

    module->to(torch::kUInt8);

    module->forward(input);

    return 0;
}
```

Error log:
```
[W upsampling.h:67] Warning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.  (function _interp_output_size)
terminate called after throwing an instance of 'c10::Error'
  what():  input_width > 0 && output_width > 0 INTERNAL ASSERT FAILED at "/home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/native/quantized/cpu/UpSampleNearest3d.cpp":158, please report a bug to PyTorch. 
Exception raised from _upsample_nearest3d_quantized_cpu at /home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/native/quantized/cpu/UpSampleNearest3d.cpp:158 (most recent call first):
frame #0: <unknown function> + 0x1e7a6e (0x7f28dc510a6e in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #1: <unknown function> + 0x1e7a20 (0x7f28dc510a20 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #2: <unknown function> + 0x1e79b0 (0x7f28dc5109b0 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #3: <unknown function> + 0x1e7860 (0x7f28dc510860 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #4: <unknown function> + 0x1e9286 (0x7f28dc512286 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #5: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x1b7 (0x7f28dc50e4d7 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #6: c10::detail::torchCheckFail(char const*, char const*, unsigned int, char const*) + 0x259 (0x7f28dc507ca9 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #7: ./reproduce_upsample() [0x40d43e]
frame #8: <unknown function> + 0x5b888fa (0x7f28e22978fa in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #9: at::native::upsample_nearest3d_quantized_cpu(at::Tensor const&, c10::ArrayRef<long>, std::optional<double>, std::optional<double>, std::optional<double>) + 0x637 (0x7f28e2296b77 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #10: <unknown function> + 0xce40b6b (0x7f28e954fb6b in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #11: <unknown function> + 0xce41c6c (0x7f28e9550c6c in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #12: <unknown function> + 0x6868638 (0x7f28e2f77638 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #13: <unknown function> + 0x6867621 (0x7f28e2f76621 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #14: at::_ops::upsample_nearest3d::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, std::optional<double>, std::optional<double>, std::optional<double>) + 0xbe7 (0x7f28e2c9e3b7 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #15: <unknown function> + 0x119ad99d (0x7f28ee0bc99d in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #16: <unknown function> + 0x117b05ac (0x7f28edebf5ac in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #17: <unknown function> + 0x117aee59 (0x7f28edebde59 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #18: <unknown function> + 0x117b1597 (0x7f28edec0597 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #19: <unknown function> + 0x6868638 (0x7f28e2f77638 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #20: at::_ops::upsample_nearest3d::call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, std::optional<double>, std::optional<double>, std::optional<double>) + 0x19ff (0x7f28e2a43a5f in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #21: <unknown function> + 0x534d05f (0x7f28e1a5c05f in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #22: <unknown function> + 0x189bb906 (0x7f28f50ca906 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #23: torch::nn::UpsampleImpl::forward(at::Tensor const&) + 0x509 (0x7f28f50c74b9 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #24: main + 0x814 (0x407d94 in ./reproduce_upsample)
frame #25: <unknown function> + 0x29d90 (0x7f28d2965d90 in /lib/x86_64-linux-gnu/libc.so.6)
frame #26: __libc_start_main + 0x80 (0x7f28d2965e40 in /lib/x86_64-linux-gnu/libc.so.6)
frame #27: _start + 0x25 (0x4074b5 in ./reproduce_upsample)

Aborted (core dumped)
```

### Versions

PyTorch version: 2.3.0a0+git03969cb
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: 12.0.1
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.12.0 | packaged by Anaconda, Inc. | (main, Oct  2 2023, 17:29:18) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-89-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:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          2
Stepping:                           7
CPU max MHz:                        4000.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           6000.00
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 vmx 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 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.5 MiB (48 instances)
L1i cache:                          1.5 MiB (48 instances)
L2 cache:                           48 MiB (48 instances)
L3 cache:                           71.5 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-23,48-71
NUMA node1 CPU(s):                  24-47,72-95
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
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:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.2
[pip3] optree==0.10.0
[pip3] torch==2.3.0a0+git03969cb
[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                     2.3.0a0+git03969cb           dev_0    <develop>

cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel