Repository: pytorch
Issue: RuntimeError: createStatus == pytorch_qnnp_status_success INTERNAL ASSERT FAILED at "../aten/src/ATen/native/quantized/cpu/BinaryOps.cpp":204, please report a bug to PyTorch. failed to create QNNPACK Add operator
Labels: oncall: jit,oncall: quantization

### 🐛 Describe the bug

 INTERNAL ASSERT Error would be raised when using `quantized tensor` (empty data) and `torch.jit.trace`. The code is as follows:

```python
import torch
import os
import ctypes

# Initialize the environment
def setup_nnapi():
    torch.backends.quantized.engine = 'qnnpack'
    libneuralnetworks_path = os.environ.get('LIBNEURALNETWORKS_PATH')
    if libneuralnetworks_path:
      ctypes.cdll.LoadLibrary(libneuralnetworks_path)

# The check function
def check(module, arg_or_args, trace_args=None):
    with torch.no_grad():
        if isinstance(arg_or_args, torch.Tensor):
          args = [arg_or_args]
        else:
          args = arg_or_args
        module.eval()
        traced = torch.jit.trace(module, trace_args or args)

# Test function
def test_qadd():
    setup_nnapi()

    func = torch.ao.nn.quantized.QFunctional()
    func.scale = 0.5
    func.zero_point = 120

    class AddMod(torch.nn.Module):
        def forward(self, lhs, rhs):
            return func.add(lhs, rhs)

    for (name, mod) in [('add', AddMod), ]:
        # check(mod(), [qpt([[1.,2.]], 0.25, 128), qpt([[1.,2.]], 0.25, 128)]) # works well
        check(mod(), [qpt([[]], 0.25, 128), qpt([[]], 0.25, 128)]) # INTERNAL ASSERT FAILED


def qpt(data, scale, zero_point):
    q = torch.quantize_per_tensor(torch.tensor(data), scale, zero_point, torch.quint8)
    return q

test_qadd()
```

The error messages are as follows:

```
Error in QNNPACK: failed to create add operator with 0 channels: number of channels must be non-zero
Traceback (most recent call last):
  File "/data/test1.py", line 145, in <module>
    test_qadd()
  File "/data/test1.py", line 138, in test_qadd
    check(mod(), [qpt([[]], 0.25, 128), qpt([[]], 0.25, 128)])
  File "/data/test1.py", line 119, in check
    traced = torch.jit.trace(module, trace_args or args)
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/jit/_trace.py", line 1002, in trace
    traced_func = _trace_impl(
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/jit/_trace.py", line 698, in _trace_impl
    return trace_module(
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/jit/_trace.py", line 1278, in trace_module
    module._c._create_method_from_trace(
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1726, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/data/test1.py", line 132, in forward
    return func.add(lhs, rhs)
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py", line 241, in add
    r = ops.quantized.add(x, y, scale=self.scale, zero_point=self.zero_point)
  File "/data/anacondas/envs/torchtest/lib/python3.10/site-packages/torch/_ops.py", line 1113, in __call__
    return self._op(*args, **(kwargs or {}))
RuntimeError: createStatus == pytorch_qnnp_status_success INTERNAL ASSERT FAILED at "../aten/src/ATen/native/quantized/cpu/BinaryOps.cpp":204, please report a bug to PyTorch. failed to create QNNPACK Add operator

```

The error is reproducible with the nightly-build version `2.5.0.dev20240815+cpu` . Please find the colab [here](https://colab.research.google.com/drive/1rExY29Vt4rvBszxV8yu86Dv34YUCLvm9?usp=sharing).

### Versions

PyTorch version: 2.5.0.dev20240815+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.3 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: 10.0.0-4ubuntu1
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-107-generic-x86_64-with-glibc2.31
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 3090
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 535.183.01
cuDNN version: Could not collect
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:                      46 bits physical, 57 bits virtual
CPU(s):                             64
On-line CPU(s) list:                0-63
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              106
Model name:                         Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz
Stepping:                           6
CPU MHz:                            800.000
CPU max MHz:                        3500.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5800.00
Virtualization:                     VT-x
L1d cache:                          1.5 MiB
L1i cache:                          1 MiB
L2 cache:                           40 MiB
L3 cache:                           48 MiB
NUMA node0 CPU(s):                  0-15,32-47
NUMA node1 CPU(s):                  16-31,48-63
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; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
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 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 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 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 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

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] onnx==1.16.2
[pip3] onnxruntime==1.19.0
[pip3] onnxscript==0.1.0.dev20240816
[pip3] pytorch-triton==3.0.0+dedb7bdf33
[pip3] torch==2.5.0.dev20240815+cu121
[pip3] torch-xla==2.4.0
[pip3] torch_xla_cuda_plugin==2.4.0
[pip3] torchaudio==2.4.0.dev20240815+cu121
[pip3] torchvision==0.20.0.dev20240815+cu121
[pip3] triton==3.0.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch-triton            3.0.0+dedb7bdf33          pypi_0    pypi
[conda] torch                     2.5.0.dev20240815+cu121          pypi_0    pypi
[conda] torch-xla                 2.4.0                    pypi_0    pypi
[conda] torch-xla-cuda-plugin     2.4.0                    pypi_0    pypi
[conda] torchaudio                2.4.0.dev20240815+cu121          pypi_0    pypi
[conda] torchvision               0.20.0.dev20240815+cu121          pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi


cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @Xia-Weiwen @leslie-fang-intel @msaroufim